In order to reduce the impact of continuous data noise and loss, a dynamic fusion local outlier factor(DFLOF) method is proposed for data-cleaning of the municipal wastewater treatment process(WWTP). First, a data dynamic segmentation method based on sliding window is designed to obtain the abnormal attribute of each segment, including mean value, maximum value and peak interval. Then, a data reliability evaluation model based on the DFLOF is established to evaluate each data segment by using the dynamic fusion local outlier factor algorithm, which improves the accuracy of abnormal data detection and elimination. Finally, a data compensation method based on radial basis function neural network is proposed to compensate the missing data and further realize the data-cleaning of the WWTP. The proposed cleaning method is applied to a real WWTP, the experimental results show that the data-cleaning method based on the dynamic fusion local outlier factor is able to clear abnormal data and improve the data quality.