Accurately identifying the main controlling factors of the fracturing performance in coalbed methane（CBM）wells and then effectively guiding the optimization of repeated fracturing schemes are the keys to improving the repeated fracturing productivity in CBM wells. Relying on the geological and engineering data in the research block，the feature selection based on Copula mutual information and cross-validation（CBFS-CV）algorithm was adopted to identify the main controlling factors that affect the fracturing performance. In combination with the gradient boosting regression model for productivity prediction and inspection，an improved identification algorithm was formed for CBM wells. This algorithm can effectively reduce the redundant features and increase correlation，and thus determine the optimal number of features. The results show that the coal structure，reservoir parameters（gas content，gas saturation，critical reservoir ratio），and operation displacement parameters（maximum operation displacement）are the three main controlling factors that affect the fracturing performance in the research block. The gradient boosting regression model verifies that the prediction coincidence rate of the main controlling factors identified by the CBFS-CV algorithm reaches 88%，which proves the effectiveness of the algorithm. Moreover，the main controlling factors of the typical well in this block were analyzed based on the above results，and the plugging removal solution with nitrogen foam was applied to the problems of poor coal structure and coal powder plugging. After field operation，the daily gas production increased from 288 m3/d to 805 m3/d，and the fracturing performance was significantly improved.