Citation count is always used to evaluate the scholar influence of a paper as more citations probably mean more endorsements received. Citation count is based on two questionable assumptions: the equal contribution assumption (i.e., each citation contributes equally to the citing paper) and the positive endorsement assumption (i.e., each citation is viewed as an endorsement from the citing paper to the cited work). Obviously, neither of these assumptions hold true. In this study, citation count are merged with two components of citation content analysis–purpose, which is the reason for the citation, and polarity, which reflects the author’s attitude toward the cited work – to provide a new convincing perspective for highly cited paper’s influence. Polarity is divided into three categories – positive, negative and neutral – and purpose into six categories – critical, comparative, used, substantiating, foundational and neutral. The full text of 100 highly cited papers and their citing papers are downloaded from Pubmed Central, then the citation contexts in citing papers are extracted and input to CNN (+Word2Vec) to identify and classify citation polarity and purpose with 89% accuracy. The result shows citations’ distributions across polarities and purposes in different periods for each paper as well as how domain, paper type (e.g. method, application, theory and review) and publish time influence distributions. Citation count, polarity distribution and purpose distribution are combined to evaluate and validate the paper’s influence based on paper type, domain and publish time.