## 4 Conclusions  
This paper suggests a novel method of feature extraction for RNN autoencoder-based meteorological disaster emergency response capability. The technique tackles the problem of feature extraction from multidimensional time series data. In order to determine the features of the disaster defense capability grade, the study first constructs a meteorological disaster capability evaluation index system that combines real data and evaluation content. Then, a strong foundation for disaster management is built using an encoder-decoder architecture to dynamically extract features from the capability sequence of the disaster management subject.  
Several experiments were planned to evaluate the effectiveness of the suggested approach. Using the KNN query, the method's performance was compared to PCA-SVM, CPCA-SVM, and CPCA-RF methods. Although none of the four methods had particularly high classification accuracy, the proposed method performed better than the other three methods when categorizing capability levels of subjects. As a result, the technique works well for feature extraction and can help with disaster management.  
The findings from our paper highlight the importance of addressing the issue of sample imbalance in evaluating meteorological disaster defense capability. The extraction of effective features is only the most basic work. In the future, we will try to build a more accurate evaluation model. Our future work involves developing and implementing solutions to address the double imbalance problem in evaluating meteorological disaster defense capability. We will accomplish this by developing an encoder-adaptive focal deep learning credit dynamic evaluation model and training algorithm, which will effectively resolve the sample proportion double imbalance problem. Additionally, we will explore the use of category weights and adaptive focal loss to address the sample percentage imbalance problem. By combining these approaches, we can improve the accuracy and performance of existing methods for evaluating disaster defense capability, ultimately leading to better disaster management and response techniques.  
However, there is still much work to be done in developing and testing methods that can handle sample imbalance more effectively. Future research in this area could focus on improving the performance and accuracy of existing methods, as well as exploring new techniques to address the issue of sample imbalance. Additionally, further investigation into the sources of information required for dynamic evaluation of capability could pro vide insights into how to better collect and parse data to improve the evaluation process. Ultimately, by continuing to develop and refine methods for evaluating meteorological disaster defense capability, we can improve disaster management and response techniques and minimize the harm caused by these catastrophic events.  
Conceptualization, J.T. and R.Y.; methodology, J.T. and G.Y.; software, J.T. and G.Y.; validation, J.T., R.Y., G.Y. and Y.M.; formal analysis, J.T. and R.Y.; investigation, J.T., R.Y. and G.Y.; resources, J.T.; data curation, J.T.; writing--original draft preparation, J.T. and G.Y.; writing--review and editing, J.T., Q.D. and R.Y.; visualization, G.Y.; supervision, Q.D. and Y.M.; project administration, Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Research project of State Grid Corporation (4000-202318098A-1-ZN).  
Not applicable.  
The authors would like to acknowledge Hohai University and State Grid Jiangsu Electric Power Company for providing the materials used for experiments. We would like to express our gratitude to the journal editors and reviewers for the recognition of this paper, the comments and suggestions which are invaluable for the improvement of our manuscript. All authors and associated individuals confirm this.  
The authors declare no conflict of interest.