Single-cell　RNA-sequencing　(scRNA-seq)　is　a　rapidly　increasing　research　area　in　biomed-ical　signal　processing.　However,　the　high　complexity　of　single-cell　data　makes　efficient　and　accurate　analysis　difficult.　To　improve　the　performance　of　single-cell　RNA　data　processing,　two　single-cell　features　calculation　method　and　corresponding　dual-input　neural　network　structures　are　proposed.　In　this　feature　extraction　and　fusion　scheme,　the　features　at　the　cluster　level　are　extracted　by　hier-archical　clustering　and　differential　gene　analysis,　and　the　features　at　the　cell　level　are　extracted　by　the　calculation　of　gene　frequency　and　cross　cell　frequency.　Our　experiments　on　COVID-19　data　demonstrate　that　the　combined　use　of　these　two　feature　achieves　great　results　and　high　robustness　for　classification　tasks.