Chinese characters are one of the logographic writing systems. There is some association between semantics and structures, shape, phonetic information of Chinese characters. In this work, multi-modal Chinese character-level embeddings are extracted, including visual features, pre-trained embeddings, shapes, and phonetic information. These embedding sequences of Chinese sentences are first fed into individual Bi-LSTM networks to capture context features, and then fused into one vector for sentiment analysis. Experimental results validate that multi-modal character-level can contribute to Chinese sentence sentiment classification. And its effect on the result is analyzed by modal features ablation test.