Only one semantic vector can be generated by word-embedding technologies such as Word2vec or GloVe for polysemous word. In order to solve the problem, a sentiment analysis model based on ELMo (Embedding from Language Model) and Multi-Scale Convolutional Neural Network (MSCNN) was proposed. Firstly, ELMo model was used to learn the pre-training corpus and generate the context-related word vectors. Compared with the traditional word embedding technology, in ELMo model, word features and context features were combined by bidirectional LSTM (Long Short-Term Memory) network to accurately express different semantics of polysemous word. Besides, due to the number of Chinese characters is much more than English characters, ELMo model is difficult to train for Chinese corpus. So the pre-trained Chinese characters were used to initialize the embedding layer of ELMo model. Compared with random initialization, the model training was able to be faster and more accurate by this method. Then, the multi-scale convolutional neural network was applied to secondly extract and fuse the features of word vectors, and generate the semantic representation for the whole sentence. Experiments were carried out on the hotel review dataset and NLPCC2014 task2 dataset. The results show that compared with the attention based bidirectional LSTM model, the proposed model obtain 1.08 percentage points improvement of the accuracy on hotel review dataset, and on NLPCC2014 task2 dataset, the proposed model gain 2.16 percentage points improvement of the accuracy compared with the hybrid model based on LSTM and CNN.