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At present, lithium-ion batteries have been widely used as energy storage systems, and they are widely used in mobile phones, electric vehicles and aircraft. However, there are certain dangers in the use of lithium ion batteries. If the battery health status (SOH) is not found in time, the danger will lead to very serious consequences. Therefore, a method for assessing the health of lithium-ion batteries based on a convolutional neural network is studied. This method uses a convolutional self-encoding neural network to extract the characteristics of the battery state data, effectively improving the accuracy of the evaluation, and the neural network can Continuous learning during the use process has high flexibility. Finally, by using the lithium battery data set published by NASA, the evaluation accuracy rate is 93.6%, which is greatly improved compared with the traditional method.