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Aiming at the problems of the existing mobile robot path optimization algorithms, such as low iteration efficiency and poor path planning ability, a machine path planning algorithm based on hopfiled neural network is proposed. Firstly, the space motion model of the robot is constructed in the world coordinate system to grasp the position information of the mobile robot at different times. Secondly, the hopfiled neural network model is constructed, and the structure of hopfiled neural network model is optimized by BP network to improve its data training ability. LSTM network structure is used to replace the hidden layer neurons, and forgetting gates, input gates and output gates are introduced to improve the generalization learning ability and sample holding ability of hopfiled neural network. Finally, the path evaluation function is introduced to evaluate the collision risk in the local area to reduce the collision probability between mobile robots. The test results show that the path planning average of the proposed improved hopfiled neural network model is 104.3m, the time is 122.1s, and the variance value of random sampling points is only 0.01, which is significantly lower than other traditional algorithms.