In order to solve the problem of how to quickly and accurately measure sheep weight dynamically and improve the intelligence of smart farms, a dynamic processing algorithm based on BP neural network is proposed. A sheep dynamic weighing system is built, and the LabVIEW host computer is used to collect data. Four pressure load cell signals are selected as network inputs, and real sheep weight data are used as network outputs, and the input and output of BP neural network are trained and tested, because there are problems such as local minimum in BP neural network, the average relative error of test samples is large. The weights and thresholds of the neural network are optimized using the particle swarm algorithm. The results show that the average relative error of the test samples of BP neural network algorithm is 7.9%, and the average relative error of the test samples of PSO-BP algorithm is 5.3%, which indicates that PSO-BP neural network is more effective in reducing the dynamic weighing error of flock and has potential application value.