Intelligent prediction algorithm combining big data and reinforcement learning and its application

In this paper, an intelligent prediction algorithm combining big data and reinforcement learning (RL) is proposed, which gives full play to the advantages of big data in rich information and reinforcement learning dynamic decision-making, so as to improve the prediction accuracy and generalization ability in complex environment. The algorithm framework consists of four core modules: data preprocessing, feature extraction, RL model construction and prediction output. Through the data preprocessing and feature extraction module, the algorithm can extract key features from massive multi-source data, and optimize the decision-making strategy by using deep Q network (DQN) to achieve accurate prediction. In the experimental part, this paper selects three data sets in the fields of finance, medical care and logistics to verify. The results show that the algorithm is significantly superior to traditional methods, such as ARIMA, LSTM and traditional Q-learning, in terms of prediction accuracy, strategy stability and cross-domain generalization ability. In the financial field, the model achieved an increase of 81% in the rate of return and an increase of $12 million in annualized excess returns. The utilization rate of beds in the medical field has increased by 19%, and the annual operating cost has been saved by 8.6 million yuan. The turnover rate in the logistics field increased by 27%, and the storage cost decreased by RMB 21 million.