This paper studies a framework for implementing meta-heuristic algorithm selection based on meta-learning approach, which is used to recommend the most suitable meta-heuristic algorithm for different problem instances instantly. Therefore, a small sample of instances for capacitated vehicle routing problem (CVRP) is selected as an experimental data set, artificial bee colony, particle swarm optimization, ant colony, artificial fish colony and genetic algorithm which are selected as the recommended algorithms. This study establishes the classification label corresponding to the problem and the algorithm by running the optimization algorithms. The meta-knowledge base corresponding to the feature and the label is generated by extracting a set of instance features. When a new instance is given, its features only need to be extracted to recommend an algorithm. In the process, three meta-learning algorithms of random forest, BP neural network and K-nearest neighbor are used to train meta-model, and comparative analysis is applied. The experimental results show that the average recommendation accuracy is about 80%. The algorithm recommendation framework based on instance features can be extended to similar applications.