In actual engineering,poor joint control parameters caneasily cause end-jitter in industrial robots. Recognizing the cause of the jitter can help locate joint anomalies and optimize control.However,there are problems with identifying the cause of jitter in industrial robots, such as high redundancy of cyclic signals, multiple jitter directions, and missing sample labels. Therefore, a method for recognizing the cause of jitter in industrial robots based on data fusion and the improved momentum contrast(MoCo)is proposed. Firstly, the data of each sensor at the end of the industrial robot are sequentially subjected to data dimensionality reduction, data expansion, horizontal splicing fusion, and dimensionality reduction to construct fusion samples that reflect sufficient and comprehensive jitter direction and state information. Data dimensionality reduction before fusion can reduce the redundancy of periodic samples and improve the efficiency of sample fusion,while dimensionality reduction after fusion can avoid the complexity of model training caused by excessively long fusion samples. Secondly, a small number of labeled fusion samples are supervised by the positive encoder classification channel output information before MoCo to guide feature clustering. Then,an improved contrastive learning strategy is implemented. The unlabeled fused data features extracted by the positive encoder arecompared with the cluster centersof the negative sample features saved by the momentum encoder, and the cluster centers with the highest feature similarity are removed to reduce the false negative sample interference of the comparison category error. And the encoder training is completed by symmetrically swapping the inputs of the two encoders for two comparison loss calculations. Finally, the cause ofjitter in industrial robots is identified by adding a Softmax classifier to the encoder classification channel. The experimental results showthat the recognition accuracy of the proposed method the causes of industrial robot jitter in different working conditions is larger than 90%, which shows the effectiveness of the method.