Query classification is a fundamental task in an e-commerce search engine, which assigns one or multiple predefined product categories in response to each search query. Taking click-through logs as training data in deep learning methods is a common and effective approach for query classification. However, the frequency distribution of queries typically has long-tail property, which means that there are few logs for most of the queries. The lack of reliable user feedback information results in worse performance of long-tail queries compared with frequent queries. To solve the above problem, we propose a novel method that leverages an auxiliary module to enhance the representations of long-tail queries by taking advantage of reliable supervised information of variant frequent queries. The long-tail queries are guided by the contrastive loss to obtain category-aligned representations in the auxiliary module, where the variant frequent queries serve as anchors in the representation space. We train our model with real-world click data from AliExpress and conduct evaluation on both offline labeled data and online AB test. The results and further analysis demonstrate the effectiveness of our proposed method.