Automatic service classification plays an important role in service discovery, selection, and composition. Recently, machine learning has been widely used in service classification. Though promising results are obtained, previous methods are merely evaluated on web services datasets with small-scale data and relatively balanced data, which limit their real-world applications. In this paper, we address the long-tailed web services classification problem with more categories and imbalanced data. Due to the long-tailed distribution of datasets, the existing machine learning and deep learning methods cannot work well. To deal with the long-tailed problem, we propose a normalized multi-head classifier learning strategy, which effectively reduces the classifier bias and benefit the generalization capacity of the extracted features. Extensive experiments are conducted on a large-scale long-tailed web services dataset, and the results show that our model outperforms the 11 compared service classification methods to a large margin.