The word2vec model is a neural network model(NNLM) that converts words in text into a word vector. It is widely used in natural language processing tasks such as emotional analysis, question-answering robot and so on. Word vectors generated for the word2vec model lacked the ambiguity of context and the inability to create OOV word vectors. Based on the similarity information of document context and word2vec model, this paper proposed a word vector generation model called the word2vec-ACV model which conformed to the meaning of OOV context. The model was similar to the process of the word vector generated by the word2vec model. First of all, base on the continuous word bag(CBOW) and the Hierarchical softmax, the word2vec model trained the word vector matrix, namely the weight matrix. Secondly, normalized the co-occurrence matrix to get the average context word vector. Then, the word vector consisted of an average context word vector matrix. Finally, it multiplied the average context word vector matrix and the weight matrix to get the word vector matrix. In order to simultaneously solve the ambiguity problem of out of vocabulary words and out of vocabulary words to create, this paper divided the average context word vectors into the global average context word vector(global ACV) and the local average context word vector(local ACV). In addition, the two taken the weight value to form a new average context word vector matrix. The word2vec model could effectively express the word in vector form. Experiments on analogical tasks and named entity recognition(NER) tasks respectively, the results show that the word2vec-ACV model is superior to the word2vec model in the accurate expression of the word vector. It is a word vector representation method to create a contextual context for OOV words.