Abstract: Community detection is of great significance for exploring the structural characteristics of complex networks while the performance of community detection algorithm makes important influence on the detection results. At present, the benchmark networks that are used to measure the performance of community detection algorithm mainly include artificial synthetic network and real-world network. Synthetic network has become the main method to measure the performance of the algorithm since the real-world network usually lacks information of known community structure. However, it is found that the microscopic characteristics of the network is unadjusted, which is different from the real-world network, the discrimination of the detection algorithm is not high, and it is inability to change the local network structure. In order to improve the performance of artificial synthetic network, a benchmark network construction algorithm on null-model is proposed. Firstly, an algorithm of null model that can maintain the mesoscale characteristics is built to improve the flexibility of network micro-feature adjustment and make it closer to the real-world network structural characteristics. Secondly, the null model of adjusting strengthen and weakness for community structure is designed for improving the evaluation accuracy of network community testing. Finally, a method based on null model is constructed so as to make some adjustments of the local topological structure for measuring the importance of the change with local community structure characteristics to the whole network structure and the performance on detection algorithm. Experimental results show that the algorithm in view of null model can effectively improve the diversity and flexibility of the benchmark network, thus making the network be more similar with the features of real-world network and meeting the demand for performance improvement of community detection algorithm.