Dynamic pricing analysis of redundant time of sports culture hall based on big data platform Sports play an important role in peoplexe2x80x99s physical health and mental regulation. The choice of sports venues is closely related to peoplexe2x80x99s age, income, and leisure time and consumption level. Traditional sports cultural halls have some drawbacks in operation time and pricing mode, which lead to waste of space resources, unbalanced net profit, and weak consumption intention. Based on operation mode of traditional sports cultural hall, this paper analyzes the game relationship between profit and consumption intention of sports cultural hall. By constructing a large data platform of the pricing model and using mathematical statistics and support vector machine, the dynamic pricing strategy of the spare time of sports cultural hall is formulated. By comparing different pricing strategies, the overall profit of sports cultural hall has been greatly improved. And the optimal strategy of adjusting pricing dynamically with time is obtained, which provides an effective method for research of the dynamic pricing strategy.

Deep Learning Based Dynamic Pricing Model for Hotel Revenue Management Dynamic pricing, which determines optimal prices of products or services, is a research topic that has received a considerable amount of attention from different scientific communities: operations research and management science, marketing, economics, econometrics, and computer science. In this paper, we describe a dynamic pricing system for hotel revenue management. The proposed dynamic pricing system consists of three parts. The first one analyzes a hotel and its competitors so that we can set a reasonable base price. The second part forecasts future occupancy of a hotel, in this part we propose a novel sequence learning model for occupancy prediction, which integrates Deep FactorizationMachine (DeepFM) and the seq2seq model. Compared with several classical methods for time series prediction, our proposed model can significantly improve the performance of hotel occupancy forecasting without very complex feature engineering. The third part of the dynamic pricing system adopts a Deep Neural Network (DNN) to model human expertise and make adjustment for the hotel room price according to the base price, the predicted occupancy and other relating factors. We compared our dynamic pricing system with a rule based pricing strategy made by revenue management experts for evaluation, the results show that our model can suggest more rational price adjustment and make hotel revenue management more efficient.