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In order to solve the problem of the inability of the existing network security protection system for new energy stations to meet the needs of network anomaly monitoring and alarm caused by the large-scale integration of new energy, a new energy station network security assessment method based on an improved long short-term memory network was proposed. Firstly, based on the architecture of the new energy station network system, the reasons for network security incidents were analyzed. Secondly, based on the random forest algorithm, the Gini coefficient of new energy station network traffic was solved, and then the important coefficients of all network traffic features were calculated to select important features. Finally, important features were input into the long short-term memory network, and attention mechanisms were used to adaptively allocate data time and features, strengthening the emphasis on important time and features in network traffic, thereby improving the accuracy of the model for network security assessment. The experimental results show that this method can accurately evaluate the network security status of new energy power stations. Compared with support vector machines, convolutional neural networks, and traditional long short-term memory networks, the evaluation accuracy has been improved by 12.65%, 9.34% and 8.79%, respectively, enhancing the perception, evaluation, and alarm capabilities of network security status in new energy power systems.