Abstract: In view of problem that manual trouble shooting method of electro-hydraulic control system of hydraulic support cannot accurately locate certain random faults or individual system faults, hardware equipment of traditional electro-hydraulic control system was transformed by intelligent technique：Collection and transmission function of the electrical parameters of key components of the system was added; Construction of big data decision analysis service platform based on Hadoop was expounded from aspects of big data collection, transmission and processing; Big data fault diagnosis engine was designed which used parallel algorithm as the core to identify and diagnose various faults. Based on MapReduce, C4.5 decision tree classification algorithm was improved, the post-pruning technique was used to solve the problem of instability and being easy to overfit of the algorithm, and multi-classifier fusion technology was used to improve accuracy of the algorithm. The test results show that fault characteristic curves of electromagnetic pilot valve, controller, pressure sensor and stroke sensor extracted by C4.5 decision tree classification prediction engine have great differences, and through dynamic comparison and matching, fault type can be identified according to the change law of the fault characteristic curves.