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In response to the limitations and long time consumption in detecting displacement faults of coal mine belt conveyors, this study investigates the multi-source heterogeneous processing of fault data. Based on the data processing, an edge detection algorithm and deep detail network are combined to construct a multi-source heterogeneous data fault detection model that combines edge detection algorithm and improved deep detail network. The study first utilizes edge detection algorithms to extract edge features from conveyor images, then combines multi-source heterogeneous data, and uses an improved deep detail network for fault recognition, and constructs a fault detection model. The results show that the average detection accuracy of the detection model in the processing of belt edge image data is 95.27%, which is 5.34% and 10.21% higher than the accuracy of the object detection algorithm and K-nearest neighbor classification algorithm. The average image data recall rate of the simultaneous detection model is 93.46%, which is 4.09% and 7.18% higher than the recall rates of the object detection algorithm and K-nearest neighbor classification algorithm. This indicates that the multi-source heterogeneous data fault detection model constructed in the study can significantly improve the reliability and robustness of belt displacement detection, and has important research value and practical application prospects.