Research on Intelligent Data Mining in Ecotourism Market Trend Forecasting Intelligent Recommendation System

This paper proposes an ecotourism market trend forecasting and intelligent recommendation system based on intelligent data mining. The system consists of data collection, processing, trend forecasting and intelligent recommendation modules. First, the LSTM model is used to realize the trend forecasting of the ecotourism market and capture the nonlinear dependencies in the market time series data; secondly, the collaborative filtering algorithm is combined with user behavior data and similar user preferences to generate personalized recommendations, and the model fusion strategy is used to weightily fuse the LSTM prediction results with the collaborative filtering recommendation results to improve the recommendation accuracy and diversity. The experiment uses real ecotourism market data, user behavior data and environmental indicator data, covering a 10-year time span for verification. The results show that the LSTM model is superior to the traditional ARIMA model in market trend forecasting, and its RMSE is reduced by 15.4%, and the prediction accuracy is significantly improved. In terms of recommendation performance, the recommendation accuracy of the system is 21.8% higher than that of the traditional rule-based method, and the user satisfaction reaches 93%, indicating its adaptability and stability in diverse user demand scenarios.