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With the popularization of higher education, China’s higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students.

Career Recommendation for College Students Based on Deep Learning and Machine Learning