Word representations are crucial for many nature language processing tasks. Most of the existing approaches learn contextual information by assigning a distinct vector to each word and pay less attention to morphology. It is a problem for them to deal with large vocabularies and rare words. In this paper we propose an Adaptive Wordpiece Language Model for learning Chinese word embeddings (AWLM), as inspired by previous observation that subword units are important for improving the learning of Chinese word representation. Specifically, a novel approach called BPE+ is established to adaptively generates variable length of grams which breaks the limitation of stroke n-grams. The semantical information extraction is completed by three elaborated parts i.e., extraction of morphological information, reinforcement of fine-grained information and extraction of semantical information. Empirical results on word similarity, word analogy, text classification and question answering verify that our method significantly outperforms several state-of-the-art methods.