Most of the classification methods of difficulty movements in competitive aerobics are based on experts’ experience and manual observation, which are feasible to a certain extent, but are affected by subjective factors and difficult to cope with complex and changing combinations of movements. Therefore, a quantum genetic algorithm is proposed to classify the difficulty movements of competitive aerobics. Based on the quantum genetic algorithm to search for the optimal classification results, we analyze the main aspects of the difficulty movements, extract the key features from the difficulty movements of competitive aerobics, and transform these features into mathematical vectors as the input of the algorithm. The quantum genetic algorithm is used to find the classification result that best matches the input vectors. The final classification results are obtained by initializing the quantum population, and comparative experiments are designed, and the experimental results show that the research method has higher accuracy in classifying the difficulty movements of competitive aerobics.