To solve the problems of needing labeling a great number of training data and pre-defining relation types in relation extraction methods based on supervised learning, a method for personal relation extraction by constructing the correlation network based on word co-occurrence information and performing graph clustering analysis on the correlation network was proposed. Firstly, 500 highly related person pairs for the research of relation extraction were gotten from the news title data. Secondly, the news data which contained related person pairs were crawled and performed pre-processing, and the keywords in the sentences which contained person pairs were gotten by the Term Frequency-Inverse Document Frequency (TF-IDF). Thirdly, the correlation between the words was acquired by the words co-occurrence information, and the key-words correlation network was constructed. Finally, the personal relations were acquired by the graph clustering analysis on the correlation network. In the relation extraction experiments, compared with the traditional algorithm of Chinese relation extraction based on word co-occurrence and pattern matching technology, the precision, recall and F-score of the proposed method were improved by 5.5, 3.7 and 4.4 percentage points respectively. The experimental results show that the proposed algorithm can effectively extract abundant and high-quality personal relation data from news data without labeling training data.