The tourism industry has always been one of the pillars of the global economy and plays a key role in the economic growth and employment opportunities of countries and regions. However, this industry is also affected by market fluctuations, political and social changes, so it needs flexible and forward-looking economic management strategies. The purpose of this study is to explore the application of neural network model in tourism industry, especially in the trend prediction of economic management strategy. Based on the prediction model of BP neural network (BPNN), this paper simulates and predicts the economic management strategy component of tourism industry. According to the actual situation of economic management in tourism industry, a three-layer network model with hidden layer is established. It is found that the correlation coefficient of BPNN model used in this paper is 0.992, MAPE is only 2.778%, and MAE and RMSE are much smaller than other models. Neural network model can learn from historical data and predict future trends, which is very importafnt for the tourism industry, because the industry is affected by seasonality, market demand and changeable factors. The application of neural network model can improve the accuracy of trend prediction and provide key insights for tourism enterprises to help them adapt to market changes. Neural network model has great potential for the trend prediction and analysis of economic management strategy in tourism industry, and provides a new way for the sustainability and competitive advantage of the industry.