Finding an optimum way to identify stocks with less delisting risk is critical for every investor in the stock market. However, this procedure is often done based on personal experience, which doesn’t fully utilize the historical delisting records. This convention of selecting stocks might result in a greater loss since it merely involves subjective judgment, especially for individual investors. Our research proposes a probabilistic approach for identifying the delisting risk associated with different industry sectors, given the P/B ratio level distribution. And this research offers a customized guide for individual investors to better choose the safer investment options related to the stocks’ industry sectors. The completion of our conditional probability matrix is operated under the high-rank assumption, together with the features of Bayesian matrices. The experimental results for our domestic delisting stocks supports the validity and usefulness of our method.