## 2 Materials and Methods
### Performance Analysis of Weather Disaster Emergency Response Capability Feature Extraction Method  
This section covers data acquisition and feature extraction. (1) Data sources. To study the capability characteristics of meteorological disaster emergency capability subjects, we need the data of meteorological disaster emergency capability subjects as the research data. Through finding literature and network search, this paper mainly takes a social third-party capability information platform and government ca -pability information platform as data sources, which specifically include enterprise chacha, sky-eye check, the national subject capability information public system, the national water conservancy construction weather disaster emergency capability information platform, the national highway construction weather disaster emergency capability information management system. Additionally, we also obtained data from the national construction meteorological disaster emergency supervision public service platform, which provides information on the capacity behavior and capacity status of construction meteorological disaster emergency subjects. In this paper, from the perspective of construction meteorological disaster emergency subject behavior and state, we need to collect the capability behavior and capability state data of the capability subject, so we capture a wide range of capability information management platforms, such as the national subject capability information public system, national water construction meteorological disaster emergency capability information platform, national construction meteorological disaster emergency supervision public service platform, and construction meteorological disaster emergency subject capacity behavior and capacity status data. (2) Data collection steps. In this paper, the capability data collection is carried out by python, and the specific steps are as follows. The first step is to drop the request to the url of the target website with the help of the requests library in Python, get the response from the remote server, and get the HTML type data of the target web page. The second step is to parse and extract the capability data in the target web interface. The XPath method is used to perform gauge matching, analyze, and locate the HTML nodes, and on this basis, to parse and obtain the capability data required in this paper from the target web pages. In the third step, use the pandas library in Python to store the capability data into a local xlsx file. The data collection procedure was prepared according to the above steps. The data captured from major capability information platforms in this paper mainly include capability characteristic information, time of occurrence of behavior, subject capability level, and time of subject capability level assessment, and they form a more complete time series sample. A total of 4052 capability data points of meteorological disaster emergency subjects including survey, design, construction, supervision, and other types were obtained, and Table 3 describes the statistics of some capability features after data pre-processing. The capability rating model constructed in this paper requires each time series sample to have more complete capability characteristics and at least two records of capability rating, filtering out samples with missing or abnormal information. We combined and cleaned the raw data after data collection in order to determine each subject's meteorological disaster emergency capability sequence. Tables 3 and 4 show, respectively, statistics for some ability traits and data on the academic level of some subjects over the past few years. We were able to train and test the suggested RNN autoencoder-based feature extraction method using the cleaned data. Data on capability characteristics, behavior timing, subject capability level, and subject capability level assessment make up the majority of the information gathered from the main capability information platforms in this paper.