In order to improve the measurement accuracy of dynamic weighing and realize real-time monitoring and fine management of intelligent pasture, a dynamic weighing algorithm based on chaotic sparrow search algorithm (CSSA) to optimize LSTM neural network is proposed. The data is collected by the dynamic weighing platform, and the Kalman filter algorithm is used to process the interference data. The CSSA-LSTM neural network model is established by using the Tent mapping strategy and the sparrow search algorithm after Gaussian mutation to optimize the parameters of the LSTM neural network. The results show that the average absolute percentage error of CSSA-LSTM neural network is within 1.5%, the average absolute error is reduced by 0.874, and the root mean square error is reduced by 1.1153. The comparative experiments show that the hybrid algorithm has the smallest prediction error and effectively improves the measurement accuracy of dynamic weighing.