Recently， deep graph clustering methods have outstanding performance in graph clustering studies. However， most existing deep graph clustering methods are based on the auto-encoder framework， and are vulnerable to reconstruction strategies and graph enhancement strategies. Therefore， a deep graph clustering method based on contrastive learning was proposed， namely Multi-level Neighborhood Contrastive attribute Graph Clustering based on adaptive learning （MNCGC）. Firstly， a dual masking strategy was designed to generate an adaptive augmented graph， which combined the node importance to generate edge weights， that is， edge masking probabilities， and a fixed masking probability was set for node features for node feature masking， so as to remove redundant information in the graph and provide rich sample pairs for neighborhood contrastive learning. Then， the edge weights were introduced into the neighborhood contrastive learning， so that the enhanced neighborhood contrastive learning was used to the original graph and the augmented graph at coding level and projection level， thereby emphasizing the local information learning and the global high-level semantic information learning. Finally， self-supervised clustering and code level representation were used to promote each other， thereby further improving the clustering effect. Experimental results on three benchmark datasets including Cora， CiteSeer and PubMed show that compared with fourteen advanced methods， MNCGC method achieves optimal values in most cases across four indicators： accuracy， Normalized Mutual Information （NMI）， Adjusted Rand Index （ARI） and F1-score， fully verifying the effectiveness of the proposed method.