Research on User Demand Prediction Model of Mobile Energy Storage Equipment Based on Semantic Analysis

Accurately predicting user needs is critical to optimizing resource allocation and improving user experience. This paper proposes an innovative user demand forecasting model, which integrates a variety of advanced technologies to improve the prediction accuracy and personalized service level. In this study, the correlation matrix construction technology is introduced to quantify user behavior and device characteristics to form a user-device interaction matrix. Matrix factorization methods, such as singular value decomposition SVD, extract latent features to capture the implicit relationship between the user and the device. This process takes into account not only explicit user feedback, but also implicit semantic information. In order to enhance the expressiveness and adaptability of the model, a collaborative filtering algorithm was applied in this study to make recommendations by analyzing the similarity between users or items. User-based collaborative filtering captures like-minded user behavior patterns, while item-based collaborative filtering is better at dealing with item similarity in large-scale datasets. This two-pronged approach improves the accuracy and variety of recommendations. In addition, an Intelligent Recommendation System was developed to respond to changes in user needs in real time. The system combines historical data and instant feedback to provide users with personalized recommendations and services. In order to further improve the robustness and generalization ability of the system, a regularization technique was introduced in this study to prevent overfitting through L2 regularization and ensure the stable performance of the model on new data. Experimental results show that compared with the traditional method, the model in this study improves the accuracy of demand forecasting by 15% and the user satisfaction by 10%. This proves the effectiveness of the proposed algorithm and technology in the demand forecasting of mobile energy storage device users.