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In order to construct a high-precision prediction model of forest surface litter moisture content, this paper takes three typical forests in Harbin City, Heilongjiang Province as the research object, and uses a self-made portable meteorological factor detector for real-time detection. Three representative machine learning methods of RF ( Random Forest ), BP ( Back Propagation ), and LSTM ( Long Short Term Memory ) were used to construct litter moisture content prediction models for the three stands. Results showed that, the prediction accuracy of the prediction model constructed by different forest stands under different machine learning methods was different. The prediction accuracy of the three stands in different machine learning models was LSTM > BP > RF. LSTM had the best effect in the study of surface litter moisture content prediction model. The results of this study indicated that LSTM was outstanding in the prediction of surface litter moisture content, which had important theoretical significance and practical guiding value for improving the accuracy of surface litter moisture content model based on meteorological element method. [ABSTRACT FROM AUTHOR]