## 3 Simulation and Discussions
### Classification Algorithm and Result Analysis  
In this paper, traditional backpropagation neural network (BPNN) [20] and LSTM neural network are chosen for the classification algorithm, and the performance of each method is compared by changing the input side data. Four sets of control experiments are taken: (1) traditional wavelet energy entropy + BPNN; (2) traditional wavelet energy entropy + LSTM neural network; (3) improved wavelet energy entropy + BPNN; and (4) improved wavelet energy entropy + LSTM neural network. Figure 4: Flow chart of wavelet energy entropy method. Figure 5: Improved energy entropy of different categories. The generated 500 sets of data are divided into 300 training samples and 200 test samples, with 60 training samples and 40 test samples for each type of data. The implicit layer of BPNN is configured as 200, the maximum number of rounds is 200, and the learning rate is set to 0.001; the implicit layer of LSTM neural network is configured as 100, the maximum number of rounds is set to 100, and the learning rate is set to 0.001. Specific neural network parameters are shown in the Table 2, and the training results of each group are listed in the following Table 3. (Traditional refers to traditional wavelet energy entropy, while improved refers to improved wavelet energy entropy.) It can be seen from Table 1 that the training effect of improved wavelet energy entropy + LSTM performs the best, which has 1000 cycles and 33 s training time, both of which are the lowest; in addition, its accuracy rate reaches 98.6%, which is the highest among the four methods. At the same time, it is not difficult to find that the training accuracy of the traditional wavelet energy entropy method is much less than that of the improved wavelet energy entropy method. In the case where the selected neural network remains unchanged, the accuracy rate is improved by 3-7% after using the improved wavelet energy entropy. In addition, the use of LSTM neural network also has a significant impact on the total training time. BPNN is not suitable for dealing with long time sequences due to its inherent characteristics, so it is inferior to LSTM neural network in terms of efficiency and accuracy. For a given training data set, the LSTM training time is reduced by 12 s and 9 s for the traditional and improved algorithms, respectively, compared with their corresponding BPNN times, which proves LSTM's superior performance. The above analysis shows that compared with BPNN neural network and traditional wavelet energy entropy method, the improved wavelet energy entropy + LSTM neural network algorithm proposed in this paper has great superiority. The algorithm requires low number of layers for the implicit layer configuration, few cycles, and short training time, but the accuracy rate still remains high enough to prove the effectiveness of the improved wavelet energy entropy method. The test samples were analyzed using this method; the test results are shown in the Figure 6, and the test accuracies for each type of occupancy are shown in the Table 4. From the table, it can be found that the overall accuracy rate is 97.5%, which shows the validity and reliability of the method. However, the variability of the prediction accuracy of each category is large, due, in part, to the small number of test samples. At the same time, it can be found in the figure that the points of error in judgment are all concentrated in the stratification of each type of group. Since this classification is based on the new energy occupancy ratio, the feature information at the intersection of R1 and R2 is continuous, and the generation of misjudgment is within the acceptable range. If the percentage range is