Abstract: With the gradual deepening of ecological research, ecology has entered the era of big data. As one of the core technologies of artificial intelligence, machine learning has been widely used to efficiently process ecological big data. We systematically summarized and analyzed the relevant research and the application of machine learning in recent years. The applications of machine learning in hydrology, soil, meteorology and climate, vegetation and other factors were analyzed with examples, which were involved in many research fields, including hydrological cycle, carbon cycle, meteorological prediction, climate change, species distribution, health assessment, landscape ecology, and resource management. Finally, its future trend was prospected based on the analysis of the problems of machine learning technology in ecological research. In general, random forest and neural network are the most commonly used machine learning methods in ecological research due to their characteristics. Integrating multiple machine learning algorithms, or integrating machine learning with traditional statistical methods and ecological models, is the best solution for future machine-learning-based ecological research.

Key words: machine learning, forest ecology, assessment, forecast.