At　the　end　of　2019,　the　COVID-19　outbreak　emerged　abruptly.　Chinese　health　authorities　highlighted　the　role　of　CT　scans,　X-rays,　and　other　computerized　lung　imaging　in　aiding　COVID-19　diagnosis.　This　study　aims　to　develop　a　computer-based　system　to　assist　healthcare　professionals　in　diagnosing　COVID-19　infections　based　on　computerized　imaging　analysis.　This　approach　aims　to　alleviate　the　workload　of　COVID-19　specialists,　improving　diagnostic　and　treatment　efficiency　and　allowing　specialists　to　focus　on　devising　appropriate　patient　care　plans　promptly.　The　proposed　method　focuses　on　analyzing　COVID-19　lesion　characteristics　within　individual　CT　slices　and　their　serial　characteristics　across　CT　sequences.　This　approach　mirrors　the　diagnostic　process　of　radiologists　closely.　To　validate　our　model,　we　compiled　a　dataset　from　real　medical　diagnostic　settings,　minimizing　the　impact　of　lesion-like　artifacts.　We　conducted　a　series　of　comparative　and　ablation　experiments　to　evaluate　the　model＇s　performance.　Results　indicate　that　our　model　outperforms　the　classic　classification　models　and　other　commonly　used　models　for　COVID-19　diagnosis　on　our　constructed　dataset.