Abstract: The transient voltage stability characteristics of power systems with high proportion of new energy and DC access seem highly-dimensional nonlinear, which affects the efficiency and performance of the data-driven evaluation model. Therefore, on the premise of constructing a set of complete features suitable for scenes with high proportion of new energy and DC access, a hybrid intelligent feature selection method based on the improved Relief algorithm and the improved swarm intelligence optimization algorithm is proposed to reduce the original feature dimension and improve the efficiency and accuracy of the model stability evaluation. Firstly, the original Relief algorithm is improved by the time series layered processing, and this improved algorithm is then used to measure the effectiveness of features, eliminate the inefficient features in classification, and get the preliminary screening feature subset after dimensionality reduction; Subsequently, the search performance of the swarm intelligence optimization algorithm is enhanced by fusing the measures of feature effectiveness. Next, the enhancement algorithm is used as the optimization strategy, and the time series classification model convolution gated recurrent unit (ConvGRU) as the classifier to form a wrapped feature selection scheme based on the swarm intelligence optimization algorithm to further realize feature subset optimization. Finally, through the comparative analysis of the examples, the compression rate of the high-dimensional features in this method may reach more than 80%, and the selected feature subset is able to effectively improve the accuracy of the evaluation model, which verifies the effectiveness and necessity of this method for high-dimensional time series features selection.