Person aspect term extraction aims to extract various attributes of individuals such as gender and nationality from their descriptions. Existing extraction methods typically train sequence labeling models on distantly-supervised data to obtain the extraction model. However, this approach has issues with inaccurate annotations and overlapping different attribute values in the data, and lacks scalability and generalizability in their models. To solve the problems, this article proposes to transform this task into a machine reading comprehension (MRC) problem, that is, to fill in the person attribute-value table by reading the person profile. This paper constructs a person attribute recognition data based on the reading comprehension framework from the person encyclopedia, and constructs two baseline models of bidirectional encoder representations from transformers-machine reading comprehension (BERT-MRC) and bidirectional encoder representations from transformers-conditional random field-machine reading comprehension (BERT-CRF-MRC). Among them, BERT-CRF-MRC is three percentage points higher than BERT-MRC on average in F1 score and the experimental results of BERT-CRF-MRC are about 92% F1 average in short text person profiles while about 75% in long text person profiles. The constructed data and code are exposed on Github.