Optimization and application of machine learning algorithms in hotel dynamic revenue management systems

This paper aims to discuss the optimization and application of machine learning algorithms in hotel dynamic revenue management system. With the rapid development of the tourism industry and the intensification of market competition, traditional revenue management methods have gradually shown their limitations in dealing with the complex and changing market environment and consumer behavior. Machine learning technology, with its powerful data processing and modeling capabilities, provides hotels with more accurate and efficient solutions in key aspects such as demand forecasting, dynamic pricing and inventory control. This paper first introduces the basic principles of dynamic revenue management in hotels and the problems of traditional methods, and then focuses on the specific applications and optimization methods of machine learning algorithms in demand forecast model optimization, pricing strategy automation and inventory management, and discusses its implementation effects and the current technical and practical challenges through actual cases. The future development direction of machine learning technology in the field of hotel revenue management is outlooked, and it is pointed out that it has a broad application prospect in promoting the intelligent and refined operation of the industry.