In the process of language recognition,a m ultilingual recognition model using convolutional recurrent neural network (CRNN) hybrid neural network is proposed to extract spatial features and temporal features of speech signals and improve the accuracy of multilingual recognition.The model firstly extracts the acoustic features of speech signals.Then the features are input into the convolutional neural networ k (CNN) to extract the low- dimensional spatial features.Next the spatial features are structured through t he spatial pyramid pooling layer (SPP layer),and the fixed length one-dimensional features are obtained.Fina lly,it is input into the recurrent neural network (RNN) to identify the language information.To test and verif y the robustness of the model,experiments are conducted on three data sets,the results show that compared with the conventional convolution neural network and recurrent neural network,CRNN hybrid neur al network language recognition accuracy of different data sets are increased,the data se ts in eight languages with time about 5 s have the voice of the most obvious,which are increased by 5.3% and 6.1 % respectively.