In order to solve the problems of traditional residual film pollution investigation, such as time-consuming manual identification of mulch film, high labor intensity and larger human error, based on UAV multispectral fusion imagery, using maximum likelihood classification (ML), minimum distance classification (MD) and spectral angle mapper classification (SAM) in supervised classification, the residual film images of four degradation films in cotton field were classified, and the degradation rate estimation model was constructed by combining Bayesian ridge regression (BRR), support vector regression (SVR) and K nearest neighbor regression (KNNR) modeling methods, so as to realize the rapid investigation of the degradation of degradation film in cotton field. The results showed that ML had a better effect on the classification of degradable films than MD and SAM, with an average error of less than 0.023 and a correlation coefficient higher than 0.9 with the measured results. Combined with different machine learning algorithms to construct the model, the ML-BRR degradation rate estimation model had the best fitting effect and generalization ability, and the R2 of the training set and testing set were 0.756~0.966 and 0.823~0.921, respectively, and RMSE were not more than 2.698% and 3.098%, respectively. Based on UAV multispectral fusion images, the maximum likelihood classifier was used to classify residual film and soil, and the degradation rate estimation model was constructed in combination with BRR algorithm, which was feasible to realize the rapid diagnosis of degradation of degradable film in cotton field, so as to provide an idea for the rapid investigation of residual film and provide reference materials for the improvement of residual film pollution control measures.