How to automatically extract valuable topic information from massive texts has become an important technical challenge. Currently, most methods carry out their research under the assumption that topics are independent. However, there are complicated inherent relationships between topics. In order to solve the abovementioned problem, we combine the correlated theory with an improved graph analytical approach to model topic detection based on topic correlation and term co-occurrence. Semantic information with high accuracy and potential co-occurrence relationship are simultaneously considered for topic detection to discover important and meaningful topics and trends. Simulation results verify the validity of the proposed model.