## 3 Results and Discussions  
Designers could indeed extract a sequence of features from every data movement segmentation for agricultural higher vocational courses based on the BP neural network, which, again, makes up a comprehensive data set. Simultaneously, designers can also move every window so that each frame can be considered a relatively small method of sampling for easier analysis. Because the extracted proportion, on the whole, differs, so does the length of every window, and the number of truncations. These measurement parameters are all from sampled sizes and are all the same. In using these subsets, we can obtain a sense of the entire network source and the target value of a BP neural network; the subsamples are represented in Figure 2. It should be noted that using intuitive filtration samples is not recommended. Figure 2: Performance analysis for the data movement segmentation of agricultural higher vocational courses based on a BP Neural network. The increased value represents the initial point so that if the variability is not quite so large, indicating a relatively smooth transition, it represents the data movement point. Figure 3 shows that when the relevant parameters are compared to the algorithmic effects, the BP-based benefits of the innovative algorithm described in this work shift its standard values to an advanced level. Within that figure, designers also see that the other algorithms, namely I-SC, I-VDT, and NN-RA, all display a few incorrect analyses and are unable to accurately determine the specific progression of the data movement signal. As a result of the high recall, accuracy, and F1 scores for the beginners, medium, and high data transfer signals, the adaptive BP-NN computation significantly outperformed the I-VDT, I-SC, and NN-RA algorithms. Adding white noise to the internal standards of this study allowed for a deeper investigation into the pros and cons of these four algorithms, and the results of the comparison are shown in Figure 4. The input data of a data movement signal, as well as its consistency and flare features, are all taken into consideration by the adaptive BP neural network algorithm, as shown in the image above. Data classification simulations demonstrate the responsiveness and effectiveness of the BP neural network algorithm. Figure 4: Performance analysis comparison of higher education vocational courses with different signal-to-noise ratios. Figure 3: Classification performance on the data tracking the higher education neural network dataset. Measures of performance for each algorithm are displayed in Figure 5; the classification performance of our BP neural network was superior to that of the other three algorithms in terms of recall and accuracy. The F1 attributes for stimulation and validity have been especially highlighted in the comparison of all three categories using three various input parameters, as can be seen in Figure 5, suggesting that the F1 mean value was higher than the remaining two inputs for both stimulation and significance. As a result, it also suggests that consolidated features provide a much higher benefit to the performance enhancement of the model. This is because the representative sample of a high arousal classification is much smaller than the sample size of all the other two categories, which results in the model being unable to comprehensively learn the factors that should be considered for this classification, and was thereby unable to differentiate this classification properly. In Figure 5, the F1 values for the medium stimulation category are also both lower than those of the higher and lower stimulation categories, which means that the model becomes less able to identify moderate stimulation than the other two stimulation categories.