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In the large data environment, the amount of mobile e-commerce information increases sharply and highlights the characteristics of users’ location. How to utilize the user's location quickly and effectively to find the valuable information of the users and supply them with the decision-making service becomes one of the important subjects of the mobile electronic commerce in recent years. Based on the traditional Monte Carlo algorithm, this paper proposes an improved Monte Carlo algorithm, a fast Monte Carlo algorithm, which can effectively solve the problem of particle degradation in the traditional algorithm. Finally, the simulation experiment is carried out. Compared with the traditional information fusion method, the experiment demonstrates that the fusion method has high accuracy and reduces the complexity of the system algorithm, so that the e-commerce platform can predict the users’ behavior information more accurately and can provide decision-making services more appropriately. Introduction With the increasing amount of mobile e-commerce data and the diversification of data structure, there comes the era of large data. How to find the effective data quickly and effectively in massive, heterogeneous or non-heterogeneous data which directly or indirectly influences the needs of users plays an increasingly important role. In particular, in recent years, based on the combination of location-based services and mobile e-commerce, to provide the decisionmaking for the users based on users’ location becomes one of the important trends of the development of mobile e-commerce in the future. [1] How to adapt to the users’ mufti-source location [2] and provide users with decision-making has become the most conspicuous technical points. In this paper, a mufti-source information fusion method based on improved Monte Carlo algorithm which is proposed to improve the value of mufti-user location information and promote the development of mobile e-commerce push service in the large data environment has great practical significance. Traditional Monte Carlo Algorithm Monte Carlo algorithm, also known as statistical simulation method, random sampling technology, is a stochastic simulation method, based on probability and statistical theory, uses random number (or pseudo-random number) to solve a lot of problems of calculation [3]. When the solution is the probability of a random event, or the expected value of a random variable, the probability of this random event is estimated by the frequency of the occurrence of such an event by some "experimental" approach, or getting some of the numerical features of the random which are treated as a solution to the problem. The essence of the Monte Carlo algorithm is the deformation of the Bayesian probability algorithm, which is based on a set of discrete particles with weights to simulate the posterior probability density of the estimated system state and to predict the state of the system, the system provides the corresponding decision-making support. In this paper, the users’ location information in mobile e-commerce is sampled and the user’s location information which is improved by Monte Carlo algorithm is fused so that it can intelligently provide the decision-making support for the e-commerce users [4,5]. 1049 7th International Conference on Education, Management, Computer and Society (EMCS 2017) Advances in Computer Science Research (ACSR), volume 61

Research on Information Fusion Method of Mobile Commerce based on Monte Carlo Algorithm under Big Data Environment