Background　and　Objective:　Virus　identification　in　electron　microscopy　(EM)　images　is　considered　as　one　of　the　front-line　method　in　pathogen　diagnosis　and　re-emerging　infectious　agents.　However,　the　existing　methods　either　focused　on　the　detection　of　a　single　virus　or　required　large　amounts　of　manual　labeling　work　to　segment　virus.　In　this　work,　we　focus　on　the　task　of　virus　classification　and　propose　an　effective　and　simple　method　to　identify　different　viruses.　Methods:　We　put　forward　a　residual　mixed　attention　network　(RMAN)　for　virus　classification.　The　proposed　network　uses　channel　attention,　bottom-up　and　top-down　attention,　and　incorporates　a　residual　architecture　in　an　end-to-end　training　manner,　which　is　suitable　for　dealing　with　EM　virus　images　and　reducing　the　burden　of　manual　annotation.　Results:　We　validate　the　proposed　network　through　extensive　experiments　on　a　transmission　electron　microscopy　virus　image　dataset.　The　top-1　error　rate　of　our　RMAN　on　12　virus　classes　is　4.285%,　which　surpasses　that　of　state-of-the-art　networks　and　even　human　experts.　In　addition,　the　ablation　study　and　the　visualization　of　class　activation　mapping　(CAM)　further　demonstrate　the　effectiveness　of　our　method.　Conclusions:　The　proposed　automated　method　contributes　to　the　development　of　medical　virology,　which　provides　virologists　with　a　high-accuracy　approach　to　recognize　viruses　as　well　as　assist　in　the　diagnosis　of　viruses.　(c)　2020　Elsevier　B.V.　All　rights　reserved.