The conventional Chinese word embedding model is similar to the English word embedding model in modeling text, simply uses the Chinese word or character as the minimum processing unit of the text, without using the semantic information about Chinese characters and the radicals in Chinese words. To this end, we proposed a radical enhanced Chinese word embedding in this paper. The model uses conversion and radical escaping mechanisms to extract the intrinsic information in Chinese corpus. Through the improved parallel dual-channel network model on a CBOW-like model, the word information context is used together with the Chinese character radical information context to predict the target word. Therefore, the word vector generated by the model can fully reflect the semantic information contained in the radicals. Compared with other similar models by word analogy and similarity experiments, the results showed that our model has effectively improved the accuracy of word vector expression and the direct relevance of similar words.