The meteorological data is multi-element time series. In order to solve the problems of large prediction error and insufficient time feature extraction of traditional temperature prediction algorithm, a GRA-Conv-BiLSTM temperature prediction method is proposed by integrating grey correlation analysis, ConvLSTM and BiLSTM together. The grey correlation analysis method is used to solve the problem of difficult parameter selection in the traditional methods, and the time window is set. The grey correlation analysis method solves the problem of difficult parameter selection in traditional methods. The time window is set, combined with the historical temperature as the input of the model, the prediction model of ConvLSTM and BiLSTM dynamic weighted fusion is established to enhance the spatio-temporal feature extraction ability of the model, and the experiment is carried out with the historical data of a meteorological station in Sichuan Province as a sample. The results show that for the multivariate meteorological time series with a large amount of data, the model shows stronger advantages, can adapt to dynamic nonlinear changes and has higher prediction accuracy.