Research on the Efficacy Assessment of Civil Aviation Risk Control Measures Based on GloVe-textCNN

With the development of intelligent transportation systems, airports are an important component of intelligent transportation systems. In this context, the importance of airport risk management has become a hot topic. Effective risk management enhances airport safety and optimizes the operational efficiency and reliability of the overall transportation system. Thus, this paper studies airport risk management methods and strategies. This study proposes a model to assess the efficacy of risk management measures at an airport to solve the airport's safety management. Firstly, the hazard data of several airports are collected and analyzed. Then, Global Vectors for Word Representation (GloVe) is used to process the text of management measures into a word vector, and we proposed a text classification model based on Text Convolutional Neural Network(TextCNN) for the risk management measures. This study proposes a model to assess the efficacy of risk management measures at an airport to solve the risk management of the airport. We use the model to classify and analyze the risk management measures. Simulation results show the performance of our approach. The results show that the accuracy of the efficacy assessment model based on TextCNN is 74.36%, while that of the model based on TextRNN is 69.24%. Thus, TextCNN is more suitable for this type of evaluation task.