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Phishing is increasingly severe in recent years, which seriously threatens the privacy and property security of netizens. Phishing is essentially a counterfeiting of brands. In order to effectively cheat the victim, phishing sites are visually and semantically highly similar to real sites. In recent years, anti-phishing methods based on machine learning are mainstream anti-phishing methods. The effectiveness of the machine learning models hinges on the extracted statistical features. However, the extracted statistical features mainly focus on visual similarity, stealing information and third-party services, which ignore the semantic information of web pages. Therefore, we extract a series of semantic features through word2vec to better describe the features of phishing sites, and further fuse them with other multi-scale statistical features to construct a more robust phishing detection model. The experimental results on the actual …