Predicting the job running time is beneficial to improve the scheduling performance of the system, and the clustering can help to train better prediction models. Traditional clustering algorithms are difficult to cluster similar job names. In order to better cluster similar jobs, the job name hierarchical clustering algorithm of letter-structure-number was constructed by analyzing the semantic importance of their components. Taking the real data of two supercomputers as an example, the data clustered by this algorithm was used to train the model. The experimental results show that the prediction accuracy of the model is better than that of the traditional method, and the overall prediction accuracy is 70%～80%.