Deep stochastic configuration network (DSCN) adopts a feedforward learning approach and randomly assigns node parameters based on a unique supervisory mechanism, which has universal approximation. However, in actual scenarios, the potential outliers and noise during data collection can negatively affect the classification results. To improve the performance of DSCN in solving binary classification problems, this study introduces the idea of intuitionistic fuzzy numbers based on DSCN and proposes an intuitionistic fuzzy deep stochastic configuration network (IFDSCN). Different from the standard DSCN, IFDSCN assigns an intuitionistic fuzzy number to each sample by calculating the sample membership and non-membership, and generates the optimal classifier by a weighting method to overcome the negative effect of noise and outliers on data classification. The experimental results on eight benchmark datasets show that compared to other learning models including the intuitionistic fuzzy twin support vector machine (IFTWSVM), kernel ridge regression (KRR), intuitionistic fuzzy kernel ridge regression (IFKRR), random vector functional link neural network (RVFL), and SCN, IFDSCN has better binary classification performance.