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Student test results are important basis to measure study situation. It is an important method to evaluate the quality of professional personnel training by using students' test scores. This paper construct a curriculum influence value network using the interaction relationship between the courses of electronic information engineering based on complex network theory. Then, the test scores of each subject have been entered into the curriculum influence value network to get the grade evaluation network model of students. Finally, evaluate the quality of professional personnel training of students according to the structural characteristics of the grade evaluation network model. In this paper, we use this model to evaluate the personnel training quality of 10 graduates of electronic information engineering. The results show that the grade evaluation network model can accurately measure the quality of professional personnel training of electronic information engineering students. Introduction It is an important content of the university personnel training evaluation system to base on the evaluation of student achievement. A good performance evaluation system can accurately reflect the quality of professional personnel training. The electronic information engineering of colleges has trained a large number of professionals in the field of information industry, which have the basic knowledge of electronic technology, information systems and information characteristics, etc., and can be engaged in various types of electronic equipment and information systems research, design, application and development, etc. At present, the research on the evaluation theory of College Students' examination and evaluation is focused on the quality evaluation of electronic information. The evaluation methods are diversified, the content of evaluation is flexible, and the evaluation standard is not unified. The vast majority of evaluation methods attach great importance to the process of evaluation and the development of the scale. But the key project score of the treatment and evaluation is still very rough. More methods are still in place to the static test ranking, mean, standard deviation, and some even avoid grade evaluation [1-7]. In this paper, we use the influence relationship between each course and the results of each course to establish the network model of student achievement evaluation, according to the structure of the network model to measure the quality of professional personnel training. Construction of the Complex Network Model of Grade Evaluation of Electronic Information Engineering Students Courses of Electronic Information Engineering. Electronic information engineering is a subject used in modern technology such as computer and electronic information control and information processing. The main research are information acquisition and processing, electronic equipment and information systems design, development, application and integration. It set up a total of 70 multi course include the circuit theory, information theory and coding, signal and system, digital signal processing, electromagnetic theory, automatic control principle, etc. We have selected 66 important 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0554 courses which affect the relationship between the students and the students, specific courses as shown in Table 1. Table 1 Courses of electronic information engineering No. Course name No. Course name No. Course name 1 Basic manufacturing technology practice 23 Linear Algebra 45 Industrial field bus control technology 2 VC++ practice 24 Probability and Statistics 46 Knowledge extension of signal and system theory 3 Electronic technology practice 25 Complex Analysis and Integral Transformation 47 Electronic System Design 4 EDA practice 26 Physics of college 48 DSP Technology and Application 5 Production practice 27 Physics of college experiment 49 Image Processing Technique 6 MCU interface practice 28 Analog Electronic Technique 50 Electromagnetic Field Theory 7 ARM information system practice 29 Circuit Theory 51 Electronic Measurement 8 DSP practice 30 Digital Electronic Technique 52 Television technique 9 Image processing practice 31 Analog Electronic Technique experiment 53 DSP Engineering Case Analysis 10 Electronic system engineering practice 32 Digital Electronic Technique experiment 54 Technology of Information and Coding 11 Graduation practice 33 The Professional Introduction 55 Digital Speech Processing Technique 12 Graduation design 34 Micro-Computer Principle And Application 56 Pattern Identificatio nand Application 13 Circuit CAD practice 35 The Special English 57 Multimedia technique 14 Electronic circuit practice 36 Sensors Technology 58 Digital communication system 15 Digital electronic practice 37 JaveProgram Designing 59 Optical Fiber Communication Techniques 16 Electronic CAD practice 38 VC++ Programing 60 The New technology of electronic information 17 DSP Comprehensive Practice 39 Single-chip Micro-computer And Interface Technology 61 Application Technology of Database 18 College Chinese 40 Mechanical Drawing and CAD 62 Microwave Technique 19 College Foreign Language 41 Signal and System 63 Computer Networks 20 University Computer Foundation 42 The Principle of Communication 64 Embedded Systems and Application 21 Computer programming (C language) 43 High frequency electronics Technology 65 EDA Technology and Application 22 Advanced Mathematics 44 Digital Signal Processing 66 Automatic Control Theory Construction of Curriculum Influence Value Network Model. We construct curriculum impact weight network according to the mutual influence relationship of course. The network is a directed weight network. Node in the network is a course in Table 3. The edge of the network is influenced by the relationship, which from the first courses to the application course. The value of edges are the proportion of the knowledge of first course in application course. For example, higher mathematics course is the first course of signal and system course. More than 50% knowledge of signal and system course are related to the knowledge of higher mathematics. Therefore, there is a point between the two 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0555 nodes of higher mathematics and signal and system, and the weight of edge is 0.5. The curriculum affecting value network model of electronic information engineering as shown in Fig. 1. Figure 1. Curriculum influence value network model Construction of Grade Evaluation Network Model. The curriculum influence value network reflects the influence degree of each course of electronic information engineering. Due to the different grades of different students, we combine the achievement of students and the value network to build the grade evaluation network. The grade evaluation network is divided into two kinds of positive network and reverse network. The weight of the edge of the forward network is equal to the result of the first course and the product of the weight of the edge of the network and the reverse network is equal to 1 minus the weight of the positive network. For example, the grades of all subjects of an electronic information engineering student are shown in Table 2, and then the grade evaluation networks are shown in Fig. 2. Table 2 High and low settings of predictor variables Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Grades 70 80 80 70 80 60 72 70 80 80 80 80 80 80 80 70 80 Number 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Grades 90 74 77 83 95 77 72 84 85 90 91 80 74 80 80 70 73 Number 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 Grades 80 70 80 70 80 80 60 80 62 87 80 80 60 63 80 70 68 Number 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 Grades 80 63 62 80 80 80 80 80 80 80 80 92 80 74 80 Figure 2. The forward and reverse grade evaluation networks Degree Centrality. The degree of a vertex in a network is the sum of weight of all the links connected to it [8]. For grade evaluation network, there are an out-degree kout and an in-degree kin which are, respectively, the sum of regulating and regulated relations at the node, as shown in Eq. 1. 6th International Conference on Management, Education, Information and Control (MEICI 2016) © 2016. The authors – Published by Atlantis Press 0556 1 1 , n n out in i ij j ij j i k A k A       (1) Where A is the adjacency matrix of network, Aij is an element of matrix A, Aij≠0 if there is a link from node i to node j, the value of Aij is the weight of edge.The total degree ki of a node ni can be computed by the sum of them, i.e., in out i i i k k k   . Closeness Centrality. Closeness centrality measures the mean distance from a vertex to other vertices. A path in a graph is a sequence of nodes and edges, such that a node belongs only to the edges before and after it, and no nodes are repeated [9]. A path length is the sum of edges weight in the path. The smallest sum of edges weight that have to be traversed in a network to get from one node to another is called the distance between the two nodes and a path through the network that achieves that distance is called the shortest path between the nodes. In a teaching evaluation network, there are in-closeness and out-closeness which are, respectively, measures the distance with regulating and regulated nodes. We define dij is the shortest path length from i to j. Then the closeness centrality of node i is the average of the inverse distances, as shown in Eq. 2. ( ) 1 1 1 in i j i ji C n d     (2) Where in i C is the in-closeness of node i. Result of Application We constructed 10 network model of electronic information engineering students using the above method and then calculated average of in-degree, in-closeness and graduation design in-close

An Evaluation Model of Academic Record of Electronic and Information Engineering Base on Complex Network Theory