Research on Consumer Behavior Analysis and Application of Mobile E-commerce Platform Based on Deep Learning

In the face of increasing market competition and increasingly diversified consumer demand, the mobile e-commerce platform urgently needs to strengthen the mining and processing of user preferences, behavior patterns and other information, in order to achieve accurate marketing and personalized recommendation, and enhance users' consumption experience. However, at present, the mobile e-commerce platform still adopts a single algorithm or model in the analysis and application of consumer behavior data, which is difficult to solve the problems of data quality and complexity of multi-dimensional analysis, and cannot meet the practical application requirements. In this regard, this paper will deeply explore the application feasibility of artificial intelligence and neural network technology in this field, and propose a brand-new data analysis and processing framework based on deep learning network to provide new ideas and methods for consumer behavior analysis and application. Practice has proved that this framework combines pre-training model and user behavior sequence recommendation model, which not only improves the processing ability of recommendation system for multidimensional complex data, but also enhances the adaptability to data sparseness. At the same time, the bidirectional recurrent neural network makes up for the shortcomings of the traditional recommendation model in dealing with historical behavior data sequence information, realizes the accurate analysis and understanding of the consumer behavior of the mobile e-commerce platform, and provides strong support for the marketing decision and product optimization of the mobile e-commerce platform.