Intelligent algorithm recommendation is an important branch of the research field of hyperheuristic algorithms. Its goal is to automatically select the most suitable algorithm for the problem to be solved from many "online" algorithms, thereby greatly improving the efficiency of problem solving. We propose and validate an intelligent optimization algorithm recommendation system, whose theoretical basis is the No Free Lunch theorem and Rice’s algorithm selection framework. It assumes that there is a potential correlation between problem features and algorithm performance, thus the algorithm recommendation problem can be converted into a multiclassification problem. In order to verify the assumption, the multimode resource constrained project scheduling problem is chosen as the test sample data, a number of metaheuristic optimization algorithms such as the particle swarm optimization, simulated annealing, tabu search, and artificial bee swarm, are used as the recommended algorithms, and the multiclassification strategy of support vector machine is applied to achieve algorithm classification recommendation. Crossvalidation results show that the recommendation accuracy exceeds 90% and the evaluation indicators perform well.