Research　on　intelligent　diagnosis　and　treatment　is　a　major　frontier　issue　in　the　current　era　of　medical　big　data.　For　the　global　health　crisis　COVID-19,　the　radiological　imaging　techniques　CT　can　provide　useful　and　important　information　thus　widely　preferred　due　to　its　merit　and　three-dimensional　view　of　the　lung.　However,　to　classify　the　CT-slices　to　assist　in　diagnosis,　due　to　the　annotation　by　radiologists　is　a　highly　subjective　task,　tedious　and　time-consuming　work　often　influenced　by　individual　bias　and　clinical　experiences.　Moreover,　the　current　image　classification　methods　cannot　work　well　on　the　massive　real-Time　totally　unlabeled　CT　scans.　To　address　these　challenges,　we　proposed　a　transfer　learning　method　using　self-supervised　information　to　classify　the　unlabeled　CT　images,　using　an　auxiliary　task　of　segmentation　to　improve　classification　efficiency.　We　classified　the　totally　unlabeled　CT　scans　from　Huoshenshan　Hospital　into　ordinary,　severe　and　critical　cases,　and　the　accuracy　rate　reached　86%.　The　experimental　results　show　that　the　use　of　small-sample　semi-supervised　transfer　learning　algorithm　can　be　used　in　insufficient　CT　images.　Our　framework　can　improve　the　learning　ability　and　achieve　a　higher　performance.　Extensive　experiments　on　real　CT　volumes　demonstrate　that　the　proposed　method　outperforms　most　current　models　and　advances　the　state-of-The-Art　performance.　©　2022　IEEE.