Abstract: The research of complex networks has been developing rapidly, which has had a profound impact on such disciplines as automatic control, statistical physics, computers, and management. However, there has been a lack of systematic and intuitive analysis of the development of topics in China. Taking the abstracts of the 13th National Complex Network Conference in 2017 as research object, we investigate the topic trend of the domestic complex network researches. Firstly, the text information of the abstracts are preprocessed and segmented by adding a custom dictionary and a stop word dictionary to obtain a document-word matrix. Then the LDA model is used to mine topics of the abstracts and SVD decomposition is applied to obtain the number of topics. As a result, ten topics of the conference are found through agglomerative hierarchical clustering according to the JS distance among the abstracts and four research communities involved in the conference are identified through community detection according to the JS distance among institutions. This work not only makes insight on the research trends and the popularity of different research directions in complex networks, but also provides reference institutions for new researchers to find corresponding research directions based on the results.