Aiming at the problem that the particle filter cycle life prediction algorithm has a poor longterm prediction effect on lithium iron phosphate battery, neural network is used to learn the historical data of the battery, and the training learning value is substituted as the observation value into the particle filter algorithm to modify the particle state value; for phosphoric acid In the dynamic equation of the lithium iron battery, there is no problem that the life is directly related to the observation value. The posterior probability relationship between the battery life and the capacity observation value is derived. The posterior probability density relationship under the Monte Carlo method is obtained, and the battery life is given. Forecast uncertainty expression. The experimental results show that the neural network training value is used as the observation value of the improved particle filter dynamic equation algorithm, and the method is effective and reduces the prediction error.