The mechanical structure of conventional circuit breaker is complex, and the faults caused by it are attriltuted to multi-source, so it is necessary to analyze the multi-source faults. However, the traditional multitask diagnosis method cannot deal with the interference between tasks well, which leads to the decrease of fault recognition rate. To solve this problem, a multi-fault diagnosis method based on wide-area features of vibration-current and soft sharing mechanism is proposed. Firstly, TKEO and DTM are used to achieve the accurate segmentation of vibration signal segments of the opening and closing process, and on this basis, the wide-area features of the vibration signal associated with the contact action and the attachment current signal are fused to synthesize color image samples to enrich the fault characterization information. Then, a multi-fault parallel diagnosis model is constructed based on the soft sharing mechanism of multitask learning, and automatically adjusts the weight ratio of the loss function of two tasks is automatically adjusted by adaptive weighting method to eliminate the mutual interference between tasks, thus improving the performance of fault diagnosis. Finally, examples are analyzed based on the two processes of closing and breaking respectively, and the results show that the classification accuracy of proposed method in this paper reaches 99. 78% and 99. 85% for two tasks respectively, which can effectively realize the multi-fault diagnosis of conventional circuit breakers.