For the cold start, sparse user feedback, and poor accuracy of similarity measurement in traditional article recommendation methods, this study proposes contextualized topic BERT (ctBERT), an article similarity calculation method that combines BERT with the topic model. The algorithm calculates the similarity scores between the given query and the related articles. The preprocessed articles are input into separate sub-modules for feature extraction and similarity score calculation. The similarity score is combined with the personalization score of the support set to obtain the final score. The algorithm is further improved by integrating single-sample learning into the recommendation framework. The experimental results from three different datasets show that the proposed method improves the NDCG criteria on all three datasets. For example, the NDCG@3 and NDCG@5 criteria improve by 6.1% and 7.2% respectively compared with other methods on the Aminer dataset, which validates the effectiveness of the method.