To address the problem that the existing intelligent recommendation do not consider decision cost constraints， an intelligent recommendation model incorporating decision cost constraints and a Lagrangian solution algorithm were proposed. Firstly， based on the user-item rating matrix， the SVD++ （Singular Value Decomposition Plus Plus） model was adopted to predict unknown ratings of users on items. Secondly， according to the predicted ratings， a single-objective optimization model of intelligent recommendation under decision cost and distribution diversity constraints was constructed. Thirdly， the distribution diversity constraint was relaxed into the objective function， and a Lagrangian relaxation model under decision cost constraint was established. Finally， a dual sub-gradient algorithm based on greedy strategy was designed to solve the constructed Lagrangian relaxation model efficiently. Experimental results on the MovieLens dataset show that compared with the Gurobi solver， the proposed algorithm reduces the solution time by at least 90.317% significantly， with the objective function value decreased by no more than 0.694%； compared with the LightGCN （Light Graph Convolution Network） method， the constructed model achieves higher recommendation accuracy on all test cases， and improves the distribution diversity on 77.8% of cases. The above fully verifies the comprehensive advantages of the proposed model and solution algorithm in terms of efficiency and performance.