This　report　selects　the　rice　dataset　from　UCL　to　compare　the　performance　of　several　classic　classification　algorithms　in　the　rice　classification　task,　including　linear　discriminant　analysis,　logistic　regression,　K-nearest　neighbor　KNN　classification,　and　naive　Bayes　classification.　Through　data　preprocessing　and　feature　engineering,　we　run　naive　Bayes　classifiers　under　different　prior　distributions　and　analyze　the　classification　results　in　detail.　In　addition,　we　select　evaluation　criteria　such　as　accuracy,　precision,　recall,　and　F1　score　to　compare　and　discuss　the　effectiveness　of　each　classification　algorithm.　The　final　results　show　that　the　choice　of　different　prior　distributions　also　has　a　certain　impact　on　the　classification　results,　and　the　classification　effects　of　linear　discriminant　analysis,　logistic　regression,　and　Gaussian　Bayes　are　better.　This　article　details　the　experimental　process　and　results　analysis,　providing　some　reference　value　for　how　to　classify　rice.　©　2024　SPIE.