Optimizing a stock portfolio is one of the important issues in the field of finance and investment. Most studies conducted in this area have focused on identifying top companies and determining the optimal weight of selected companies. However, identifying industries with high return potential and determining their weights can also help in recognizing stocks with desirable returns, thereby improving portfolio performance. The aim of this paper is to provide a new model for ranking and identifying industries with high growth potential in the Iranian capital market. Various factors influence the future performance identification of industries. One of the most significant of these is investor sentiment, which is assessed through technical indicators. The Dempster-Shafer evidence theory, as one of the multi-criteria decision-making methods, utilizes Bayesian probability theory and can reduce the complexity and uncertainty present in financial markets. Therefore, this research will use Dempster-Shafer theory to rank and identify top industries with the help of technical indicators. The results obtained from examining six half-years (from March 21, 2021, to March 20, 2024) for ranking ten industries, including cement, housing, pharmaceuticals, iron and steel, precious non-ferrous metals, non-metallic mineral products, chemicals, food products, automotive parts, and insurance in the Iranian capital market, indicate the predictive capability of the future performance of these industries using the proposed model. It is recommended that both institutional and individual investors first use the proposed model to identify the top industries and then form their stock portfolios based on the selected industries.