At present, there are a lot of unhealthy phenomena in the cherry market, which have seriously damaged the economic benefit of famous cherry brands. As a kind of spectrum tracing technology, Raman spectrum tracing technology has been paid more and more attention because of its advantages of fast-speed, high efficiency, pollution-free and non-destructive analysis. And the long short-term memory (LSTM) network is a kind of feedback neural network with memory, which is a variant of the recurrent neural network. LSTM network overcomes the problem of gradient disappearance in the recurrent neural network, and is suitable for solving sequence-sensitive problems and tasks. At present, it is widely used in speech recognition, image recognition and handwriting recognition. However, there are few studies on the application of LSTM network in origin tracing. Therefore, a technology that can identify cherries of different origins quickly and non-destructively is urgently needed. Based on this, this study in this paper proposes a fast and non-destructive identification technique for cherries from different origins by using LSTM network and Raman spectroscopy. In this study, 369 cherries from the United States, Shandong and Sichuan are used to obtain the spectral data of cherries from different regions with the Raman spectrometer under the 785 nm laser. Moreover, the Raman spectral data after baseline correction is taken as the network input data, and a discriminant model is built based on the LSTM network to realize rapid identification of cherries from different origins. In addition, the sample discrimination accuracy A, sample precision P, sample recall R, and sample F values are used as evaluation standards to explore the effects of different prepossessing methods on the sample discrimination accuracy. The results showed that when the ratio of the sample training set to the test set is 85∶38, the LSTM network model that directly uses the original Raman spectral data has poor ability to identify the origin, and the identification accuracy is only 79.87% on average. But when prepossessed Raman spectral data are used, the average accuracy of the model remains above 92%. And the model has the best discrimination accuracy after using Stravinsky-Golay (SG) and multiplicative scatter correction (MSC) prepossessing methods, and the discrimination accuracy reaches 99.12%. At the same time, the accuracy rate, recall rate and F value of LSTM network discrimination model are all high when the preprocessing method named SG+MSC is used. It means that the LSTM discrimination model proposed in this paper can perform well in distinguishing cherries from different regions, which provides a new way of tracing the origin of cherries.