Source code comments is an important part of the software, so researchers need to use manual or automated methods to generate comments. In the past, the quality assessment of this kind of comments is done manually, which is inefficient and not objective. In order to solve this problem, an assessment criterion was built in which four aspects of the comments including comment format, language form, content and code-related degree were considered. Then a code comments quality assessment method based on an aggregation of classification algorithms was proposed, in which machine learning and natural language processing technology were introduced into comments quality assessment, by using classification algorithms the comments were classified into four levels, including unqualified, qualified, good and excellent ones. The evaluation results were improved by the aggregation of the basic classification algorithms. The precision and F1 measure of the aggregated classification algorithm were improved about 20 percentage points compared with using a single classification algorithm, and all the indexes have reached more than 70% except the macro average F1 measure. The experimental results show that this method can be applied to assess the quality of comments effectively.