Egg　freshness　grade　evaluation　is　an　important　technical　indicator　in　process　of　egg　quality　inspection.　Egg　samples　from　different　storage　environment　were　prepared,　the　hyperspectral　image　information　and　spectral　information　of　eggs　were　collected,　and　the　image　features　and　spectral　features　were　extracted.　The　image　features　and　spectral　features　were　integrated　with　parallel　integration　method,　and　the　features　were　extracted　based　on　successive　projections　algorithm　and　gray-level　co-occurrence　matrix　method.　The　support　vector　machine　egg　freshness　discriminant　model　was　built.　The　model　was　optimized　by　particle　swarm　optimization　algorithm,　the　accuracy　rate　of　training　set　reached　up　to　85%,　and　the　accuracy　rate　of　prediction　set　reached　up　to　76.　67%　.　In　order　to　solve　the　occasional　misjudgment　of　single　model,　the　progressive　features　integration　method　was　used,　and　the　multi-model　consensus　strategy　and　deep　residual　network　ResNet　50　analysis　method　were　introduced.　The　multi-model　consensus　strategy　based　on　successive　projections　algorithm-histogram　of　oriented　gradients　features　extraction　method　was　built,　the　accuracy　rate　of　training　set　of　the　model　increased　to　89%,　and　the　accuracy　rate　of　prediction　set　increased　to　88%　.　Meanwhile,　the　deep　residual　network　ResNet　50　model　based　on　successive　projections　algorithm-histogram　of　oriented　gradients　features　extraction　method　was　built,　the　accuracy　rate　of　training　set　of　the　model　increased　to　89%,　and　the　accuracy　rate　of　prediction　set　increased　to　86.　67%　.　The　image　features　and　spectral　features　integration　modelling　analysis　indicated　that　both　parallel　integration　method　and　progressive　integration　method　had　a　certain　identifiability　for　egg　freshness　grade　discrimination,　and　the　multi-model　consensus　strategy　of　progressive　integration　method　showed　better　discrimination　effect.　©　2022　Beijing　Technology　and　Business　University,　Department　of　Science　and　Technology.　All　rights　reserved.