With the development of information technology, the hotel industry needs to utilize advanced technologies and methods to improve the accuracy and efficiency of room forecasting and pricing in order to adapt to the changes in the market and the pressure of competition. This paper adopts a combination of quantitative and qualitative methods, following two phases(1) data analysis phase, using statistical analysis and visualization techniques to clean, process, describe, and explore the collected data related to hotel rooms; (2) model building phase, using machine learning techniques to construct and train a deep neural network model to achieve the prediction of hotel rooms, and using reinforcement learning techniques that realizes the pricing of hotel rooms. In this paper, theoretical and practical experiments are conducted to compare and evaluate with existing research methods and models from different perspectives and levels. The results show that the model proposed in this paper outperforms other models in all indicators, with higher prediction accuracy and pricing efficiency, as well as good generalization and adaptation capabilities. The research in this paper has important theoretical and practical significance for revenue management and competitive strategies in the hotel industry.