Chinese word segmentation is an important task in Chinese natural language processing. One of bottleneck problems in Chinese word segmentation is Out-Of-Vocabulary (OOV) detection. This study proposes an unsupervised OOV detection method based on improved PMI algorithm and minimum branch entropy combining strategy. Firstly, the punctuation marks and special characters which are not related in the text are removed. The improved PMI algorithm recognizes the string with strong cohesion in the text, and gets the candidate OOV through the filtering of the stop word list and the core vocabulary. Then the minimum branch entropy of candidate OOV is calculated, when the term frequency-minimum branch entropy threshold is met, the output is the OOV. Through theoretical and experimental analysis, the algorithm can generate a personalized OOV dictionary for different texts, and does not require long-term learning and training to adjust parameters, and has a certain improvement in the accuracy and recall rate of detection.