A : A new data cleaning procedure for electron cyclotron emission imaging (ECEI) of EAST tokamak is developed. Machine learning techniques, including Support Vector Machine (SVM) and decision tree, are applied to identifying saturated, zero, and weak signals of ECEI raw data, which not only reduces the effort of researchers for data analysis, but also improves the accuracy of data preprocessing. Proper training sets are sampled using massive raw ECEI data from the EAST tokamak. Optimal window size of temporal signal, kernel function, and other model parameters are obtained by model training. With the optimized parameters, the recognition rates of saturated, zero, and weak signals in raw data are 99.4%, 99.86%, and 99.9%, respectively, which proves the accuracy of this procedure.