Abstract: OBJECTIVE To establish qualitative models of Raman spectroscopy for identification of shellfish animal drugs Ostreae Concha and Haliotidis Concha based on multiple algorithms. METHODS The Raman spectrograms of 366 samples from 32 batches of Ostreae Concha and 29 batches of Haliotidis Concha were collected respectively. The spectrograms were processed by Savitzky-Golay smoothing filter, Scale Normalization for Image Pyramids, and normalized preprocessing in Matlab. Principal component analysis was used to reduce dimension of full band and specific bands. Six classification algorithms including K-nearest neighbor, decision tree, discriminant analysis, ensemble learning, support vector machine and artificial neural network were used to establish the identification models, and the performance of the first five models was improved by Bayesian optimization. RESULTS The Raman signal peaks were obvious after preprocessing, and there was obvious clustering trend of Ostreae Concha and Haliotidis Concha after dimension reduction. Five algorithmic identification models with 200-310, 670-740 and 1050-1100 cm^-1 as characteristic bands were established. Compared with the full band model, the accuracy of all classifiers were improved except ensemble learning and support vector machines. After Bayesian optimization, the accuracy of three models achieved 98% in training set and 100% in test set. Using artificial neural network modeling for classification, the accuracy of training set, validation set and test set reached 100%, which could distinguish Ostreae Concha and Haliotidis Concha well. CONCLUSION Qualitative models of multiple algorithms by Raman spectroscopy for the identification of Ostreae Concha and Haliotidis Concha are established. The predictions of each model are satisfactory. Among them, the artificial neural network model can achieve 100% accurate and fast identification of Ostreae Concha and Haliotidis Concha.