In　order　to　build　an　education　information　platform　to　support　＂industry-education　dialogue＂and　at　the　same　time　serve　the　teaching　and　research　of　industry-education　integration.　In　this　paper,　we　use　big　data　technology　to　construct　a　system　of　education　management　and　resource　optimization,　supporting　education　construction　in　the　context　of　industrial　transformation　and　upgrading.　For　larger-scale　WSNs,　an　energy-efficient　data　collection　algorithm　based　on　non-uniform　clustering　is　proposed.　To　deal　with　large-scale　optimization　problems,　a　hybrid　algorithm　of　genetic　algorithms　and　particle　optimization　hierarchical　collaboration　is　introduced.　Then,　the　algorithm　is　applied　to　cluster　WSNs,　an　adaptation　function　is　constructed　with　the　goal　of　equalizing　energy　consumption,　and　a　data　collection　algorithm　based　on　non-uniform　clustering　is　proposed.　The　Apriori　algorithm,　which　is　based　on　the　interest　degree　model,　is　used　to　mine　the　course　evaluation　data　for　the　system　design　in　the　university　industry-teaching　fusion　model.　This　system　design　does　both　course　evaluation　and　resource　optimization.　The　system＇s　total　evaluation　score　is　high,　at　90.84　points.　It　also　improves　the　efficiency　of　cooperation　between　the　education　industry　and　schools.　It　proves　that　the　system＇s　design　is　effective　and　high-level　and　has　a　positive　effect　on　teaching　management　and　resource　optimization　in　education.　　©　2024　Ligao　Xie,　published　by　Sciendo.