With the depletion of oil resources and the aggravation of environmental pollution, electric vehicles have a growing future and will be more popular as the main force of new energy consumption. They have attracted greater attention from various countries. The sizing and location problem for charging stations has been a hot point of global research, and these issues are important for government planning for electric vehicles. In this paper, we first built a BASS model to predict the total number of electric vehicles and calculate the size of charging stations in the coming years. Moreover, we also developed a queuing model to optimize the location of charging stations and solved this issue by using the exhaustion method, which regards minimum cost as the objective function. After that, the model was tested using data from a city in China. The results show that the model in this paper is good at predicting the number of electric vehicles in the coming years and calculating the size of charging stations. At the same time, it can also optimize the distribution of charging stations and make them more balanceable. Thus, this model is beneficial for the government in planning the development of electric vehicles in the future.

Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (p-value), recall (R-value), and F-value.