Based on the importance of automatic classification of Tibetan La case example sentences in Tibetan natural language processing, according to the usage and adding rules of Tibetan La case, this paper classifies Tibetan La case example sentences and defines the classification concept, and proposes an automatic classification model of Tibetan La case example sentences with fusion dual-channel syllable features. The proposed model first uses word2vec and Glove to construct a dual-channel Tibetan syllable embedding, and combines the dual-channel syllable features in each convolution respectively to enrich the expression of input features and improve the spatial representation ability of the convolutional layer. Then in each convolution, the Bi-LSTM combined with the hierarchical attention mechanism is used to learn the timing features, and the multi-channel features are spliced to improve the learning ability of the context timing features. Finally, the automatic classification of Tibetan La case example sentences is realized through the full link layer and the Softmax layer. Experiments show that proposed model has an accuracy of 90.26% in the classification of Tibetan La case example sentences on the test set.