The invention discloses a phishing website detection method and system in an Android environment, and the method comprises the following steps: obtaining a website URL when a cellphone user browses a webpage, and transmitting the website URL to a server; obtaining the webpage contents when the server receives the URL transmitted by a client, and constructing a feature vector according to the URL and the webpage contents; carrying out the clustering of K-means through a sample dataset, forming two clustering centers, and training a naive Bayes algorithm; discriminating the feature vector through an algorithm combining the K-means and an improved naive Bayes algorithm, and transmitting a discriminating result to a client user; enabling the client user to give a corresponding prompt according to a returned result, displaying a prompt box for reminding a user if a website is a phishing website, or else browsing the webpage normally. The method makes the most of the advantages that the K-means algorithm is high in clustering speed and the naive Bayes algorithm is high in accuracy, and greatly improves the classification speed and precision.