Construction of user behavior prediction model of information management system based on deep learning

With the wide application of information management system (IMS) in various industries, the scale and complexity of user behavior data increase exponentially. Traditional user behavior prediction methods have limitations when dealing with high-dimensional, dynamic and nonlinear data. In this paper, an improved user behavior prediction model based on deep learning is constructed. Self-attention mechanism is introduced to enhance the long-term dependence capture ability, multi-input neural network is used to fuse multi-modal data, and migration and meta-learning are combined to improve the prediction accuracy in cold start scenarios, and the model's interpretability is enhanced through visual analysis. Data preprocessing and feature engineering include structured data standardization, sliding window division and interpolation of behavior sequence, semantic vector extraction of text data, etc. The model architecture design adopts the improved Transformer-Enhanced model, which includes four layers: feature coding, behavior modeling, multimodal fusion and prediction. Model training and optimization adopt a multi-task learning framework, jointly optimize the prediction targets of click-through rate, conversion rate and loss probability, and introduce dynamic curriculum learning strategies. The experiment uses data sets of e-commerce and medical scenarios to evaluate. The results show that the complete model proposed in this study is superior to the benchmark model in prediction performance, and it also shows significant advantages in cold start scenarios, and the interpretable verification case further proves the effectiveness of the model.