Feature extraction, the key process of the terahertz spectral recognition, typically uses the dimensionality reduction techniques. However, when the overall difference of terahertz spectra of some compounds is small, dimensionality reduction methods often lack important feature information of sample differences, which leads to classification errors. If the dimensionality reduction process is not performed, the traditional machine learning algorithm cannot be well classified because the original spectral data have a high dimensionality. Therefore, this paper proposes a terahertz recognition method based on bidirectional long short-term memory recurrent neural network (BLSTM-RNN), which performs automatic feature extraction with containing full spectrum information of terahertz spectrum. BLSTM-RNN is a special recurrent neural network, whose LSTM unit can be used effectively to solve the problem that the original terahertz spectral data dimension is high. Then, it becomes easier to train the model. What’s more, the architectural model combined with bi-directional spectral information can enhance the ability of the model to extract valid feature information from complex spectral data automatically. In this paper, three types and 15 compounds terahertz transmission spectra are used as test objects. The terahertz transmission spectrum samples data of 15 organic compounds such as Anthraquinone, Benomyl and Carbazole were firstly normalized in 0.9～6 THz by S-G filtering and cubic spline interpolation. Then a recurrent neural network with bidirectional Long short-term memory unit (LSTM) is constructed to automatically extract the full spectrum information of the terahertz spectrum and classify it by Softmax classifier. Through experimentation of optimizing the network structure and various parameters, the prediction model of the complex terahertz transmission spectrum data is obtained, and the comparative experiment is done by contrasting with the traditional machine learning algorithm SVM, KNN and neural network algorithm MLP, CNN. The results show that compared with other methods, the recognition accuracy of both dataset-1 and dataset-2 is improved. Dataset-1 and dataset-2 are two terahertz transmission spectral data sets of five compounds with large difference and no obvious peak characteristics, and the average recognition accuracy of the former is 100% and the latter 98.51%. Most importantly, dataset-3 is a dataset of terahertz transmission spectra with five similar spectral lines. The average recognition accuracy is 96.56%. Compared with other methods, the recognition accuracy is significantly improved. Dataset-4 as a collection of transmission spectral data sets for dataset-1, dataset-2, and dataset-3 has an average recognition accuracy of 98.87%. It is verified that the BLSTM-RNN model can automatically extract effective terahertz spectral characteristics and meanwhile ensure the prediction accuracy of complex terahertz spectra. In the selection of model training optimization algorithm, the Adam optimization algorithm is better than the RMSProp, SGD and AdaGrad optimization algorithms, and the target function loss value of the model has the fastest convergence rate. At the same time, as the number of training iterations increases, the prediction accuracy of similar terahertz transmission spectral datasets also increases. The proposed method can provide a new identification method for spectral recognition retrieval of complex terahertz spectral databases.