The operation status of the medium density fiberboard hot press machine directly affects production efficiency and product quality, in order to ensure the normal operation of the equipment and the smooth progress of production. Introduce particle swarm optimization algorithm to design a pressure fault diagnosis method for medium density fiberboard hot press machines. By collecting operation data of the press machine, establishing a membership function of pressure parameters, and applying particle swarm optimization algorithm to extract fault data, fast diagnosis and type classification of pressure faults in medium density fiberboard hot press machines are achieved by encoding the fault data and matching the corresponding information. The experimental results show that the proposed method has a fault type identification accuracy of 100%, and the similarity between the pressure fault diagnosis results and manual diagnosis results is close to 99%. The proposed method can achieve accurate diagnosis of pressure faults.