Aiming at the problem of the accuracy of the recognition of the operating state caused by the unbalanced data acquisition in the normal and blocked fault state of the drainage pipeline, a method for a pipeline clogging state recognition based on cost-sensitive support vector machine based on fruit fly optimization algorithm is proposed. According to the unbalanced data set collected under various operating conditions in the drainage pipeline, the wavelet packet decomposition is first performed on the unbalanced data set. Secondly, the energy entropy of each decomposition coefficient and the approximate entropy index are used to construct the feature vector set. The fruit fly optimization algorithm is adopted. (FOA) optimizes the penalty factor Cm and the kernel function parameter g, that is, the costsensitive support vector machine ( CS-SVM) model optimization, and inputs the feature set into the optimized CS-SVM model to normalize the drainage pipe. Blocking state recognition, by increasing the penalty cost of misclassification of a few types of samples, the results show that the recognition accuracy of a few classes is improved.