The invention discloses an office building load prediction method based on a particle swarm neural network. The method includes the following steps of: determining the input feature variable and the output target vector of an office building load prediction neural network; initializing a particle swarm solution set; calculating the fitness value of each particle; updating the local optimal position and the global optimal position of each particle; updating speeds and positions of particles; judging ending conditions; is the ending conditions are met, outputting the current optimal position; assigning the neural network and simulating the neural network, and predicting the load of an office building. Through the office building load prediction method based on the neutral network, all internal disturbance and external disturbance factors influencing fluctuation of the official building load are comprehensively considered. Meanwhile, aiming at the special periodic electricity consumption characteristic of the office building, the periodic load change is also considered; the high-precision load prediction of the office building is achieved by using manually simulating the neutral network; the office building load prediction method based on the particle swarm neural network has the advantages of high load prediction precision and simple and easy to implement.