Identification　of　fossil　species　is　crucial　to　evolutionary　studies.　Recent　advances　from　deep　learning　have　shown　promising　prospects　in　fossil　image　identification.　However,　the　quantity　and　quality　of　labelled　fossil　images　are　often　limited　due　to　fossil　preservation,　conditioned　sampling　and　expensive　and　inconsistent　label　annotation　by　domain　experts,　which　pose　great　challenges　to　training　deep　learning-based　image　classification　models.To　address　these　challenges,　we　follow　the　idea　of　the　wisdom　of　crowds　and　propose　a　multiview　ensemble　framework,　which　collects　Original　(O),　Grey　(G)　and　Skeleton　(S)　views　of　each　fossil　image　reflecting　its　different　characteristics　to　train　multiple　base　models,　and　then　makes　the　final　decision　via　soft　voting.Experiments　on　the　largest　fusulinid　dataset　with　2400　images　show　that　the　proposed　OGS　consistently　outperforms　baselines　(using　a　single　model　for　each　view),　and　obtains　superior　or　comparable　performance　compared　to　OOO　(using　three　base　models　for　three　the　same　Original　views).　Besides,　as　the　training　data　decreases,　the　proposed　framework　achieves　more　gains.　While　considering　the　identification　consistency　estimation　with　respect　to　human　experts,　OGS　receives　the　highest　agreement　with　the　original　labels　of　dataset　and　with　the　re-identifications　of　two　human　experts.　The　validation　performance　provides　a　quantitative　estimation　of　consistency　across　different　experts　and　genera.We　conclude　that　the　proposed　framework　can　present　state-of-the-art　performance　in　the　fusulinid　fossil　identification　case　study.　This　framework　is　designed　for　general　fossil　identification　and　it　is　expected　to　see　applications　to　other　fossil　datasets　in　future　work.　Notably,　the　result,　which　shows　more　performance　gains　as　train　set　size　decreases　or　over　a　smaller　imbalance　fossil　dataset,　suggests　the　potential　application　to　identify　rare　fossil　images.　The　proposed　framework　also　demonstrates　its　potential　for　assessing　and　resolving　inconsistencies　in　fossil　identification.