Abstract: The gas production of a coalbed methane well is an important indicator to measure the gas production capacity of a coalbed methane well and the effect of engineering development. Accurately predicting the daily gas production is a key issue to ensure the efficient production of coalbed methane. Taking the No.3 coal seam of Zhengzhuang block in southern Qinshui Basin as the research object, the dynamic parameters of CBM well drainage such as bottom hole flow pressure, liquid column height, casing pressure, daily water production and stroke frequency, were selected as independent variables to analyze the daily gas production. The correlation between these drainage parameters established a CBM well gas production prediction model and method based on cyclic neural network. The results show that, there is a positive correlation between the gas production of CBM well and stroke, gas production is negatively correlated with bottom hole flow pressure, casing pressure, liquid column height and daily water production. Based on feature importance analysis in deep learning random forest algorithm, the nonlinear relationship between the drainage and production parameters and daily gas production was studied and the contribution rate of drainage parameters to daily gas production in forecast model, the order of importance of the influence of drainage and production parameters on daily gas production was shown as follows: bottom hole flow pressure >liquid column height >casing pressure >daily water production > stroke frequency. On that basis, an improved prediction model of short and long time memory based on cyclic neural network, substitute the drainage and production data of Well Z4-026 into the model for calculation, the gas production of CBM Wells in the next 60 days was predicted, the prediction results were compared with the traditional support vector machine regression model, random forest regression model and BP neural network model, the prediction results are compared with the traditional support vector machine regression model, random forest regression model and BP neural network model. It is found that the improved shortand longterm memory neural network prediction model has relatively good fitting effect. The error between actual and forecast production is less than 5%. In the prediction and analysis of gas production of five CBM wells in Zhengzhuang block, the relative error is less than 10%. Therefore, this method will provide an effective way for the prediction of CBM gas production and the formulation of reasonable drainage and production system.