Smishing has always been an important means of black-gray drainage. Currently, interception measures are primarily deployed at the network communication layer of SMS transmission. In response, black-gray industry organizations continuously improve their evasion techniques to bypass the interception strategies along the communication chain. This article presents a novel phishing text message interception method that involves collaboration between the terminal and the cloud. Its advantage lies in the combination of deep learning models for text classification and the identification of reserved information. Firstly, the optimization process involves leveraging a massive corpus in the vertical domain, consisting of hundreds of millions of samples, to optimize the Bert model and TextCNN network. This optimization includes incorporating proprietary features such as statistics, pinyin, and glyph. As a result, classification models are specifically designed for both the cloud and terminals, enabling efficient recognition of ordinary phishing text messages. Secondly, to address spear-phishing attacks and various types of spoofing in phishing text messages, an enhanced named entity recognition technology is employed. This technology allows for the extraction of URLs, QQ numbers, WeChat IDs, mobile phone numbers, and other suspected fraudulent reserved information from the text messages. Subsequently, dedicated models for feature fusion of URLs and heuristic discrimination strategies for network accounts are established based on this extracted information. Combining the two aforementioned detection methods, the paper has implemented an intelligent phishing text message detection system, which has been deployed and applied in a real environment with of hundred millions users. The system achieved a recall rate of 98.32% and an accuracy rate of 99.44%.