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The neural network has good self-learning ability, powerful parallel processing capabilities and advantages that can approximate any nonlinear function. For communication equipment malfunction occurrence is random, can be affected by many factors, corresponding Troubleshooting Has a highly nonlinear and uncertainty characteristics. BP neural network algorithm, optimized GA-BP neural network algorithm and POS-BP neural network algorithm are used to Build a base station equipment fault diagnosis model Respectively, the base station equipment malfunction historical data are extracted for simulation, predicting equipment malfunction type accurately, To help raise the level of intelligence of dispatching management for Maintenance Company, improving the efficiency of the operation and maintenance of base station equipment. The Simulation results show that: BP paper, GA-BP and POS-BP neural network algorithms are able to achieve the goals of predicted the category of equipment malfunction, and the GA-BP neural network algorithm compared to BP or POS-BP neural network algorithm for communications equipment troubleshooting has better adaptability.