Word embeddings have a significant impact on natural lan-guage processing.In morpheme writing systems,most Chinese word em-beddings take a word as the basic unit,or directly use the internal structure of words.However,these models still neglect the rich rele-vant derivative meanings in the internal structure of Chinese charac-ters.Based on our observations,the relevant derivative meanings of the main-components in Chinese characters are very helpful for improving Chinese word embeddings learning.In this paper,we focus on employing the relevant derivative meanings of the main-components in the Chinese characters to train and enhance the Chinese word embeddings.To this end,we propose two main-component enhanced word embedding models named MCWE-SA and MCWE-HA respectively,which incorporate the relevant derivative meanings of the main-components during the training process based on the attention mechanism.Our models can fine-grained enhance the precision of word embeddings without generating additional vectors.Experiments on word similarity and syntactic analogy tasks are conducted to validate the feasibility of our models.Furthermore,the re-sults show that our models have a certain improvement in the similarity task over most baselines,and have nearly 3%improvement in Chinese analogical reasoning dataset compared with the state-of-the-art model.