Research on the Application of Deep Learning in the Construction of Information Resource Score Prediction Model

This paper aims to improve the accuracy and efficiency of information resource score prediction by introducing deep learning algorithm design. The study starts with the feature extraction of information resources and constructs a score prediction model based on CNN and RNN. By introducing the attention mechanism and adaptive weight allocation, the model parameters are optimized to adapt to the diversity and dynamics of complex information resources. This paper uses information resources in a certain field as a data set for model training and testing, and compares it with traditional prediction methods. Simulation results indicate that the proposed model is better than the conventional one in precision, recall rate and forecasting efficiency, especially in the case of large data sets. The average prediction error of this model is less than 2.5%, and the prediction speed is shortened by more than 30%. This study provides strong support for the optimization of information resource scoring system, and opens a new direction for the practical application of deep learning in information resource management.