To meet the requirement of China Ⅵ emission regulation, gasoline vehicles with gasoline direct injection engine must be equipped with gasoline particulate filter (GPF) to limit particulate emission. GPF fault diagnosis has become a research focus of emission control technology because of its calibration difficulty, low diagnostic frequency etc. In consideration of the nonlinear classification characteristics of neural network, a GPF fault diagnosis algorithm based on neural network was proposed. The engine steady-state condition information and corresponding pressure signals before and after GPF were collected as feature inputs to form a neural network data set according to the diagnosis principle of GPF. The system structure was optimized by applying noise and the appropriate parameters were determined through a large number of experiments to evaluate generalization capability. The evaluation results showed that the GPF diagnosis algorithm based on neural network had good accuracy and generalization performance. A GPF diagnostic algorithm test platform was developed in Ni PXI series software and hardware, and the embedding and feasibility verification of algorithm were conducted. The test results showed that the GPF diagnosis algorithm could complete the real-time diagnosis and decision-making of fault and meet the design requirements.