This　paper　provides　a　collaborative　filtering　recommendation　algorithm　based　on　the　combination　of　item　similarity　and　alternating　least　square　collaborative　filtering　based　on　the　Spark　platform.　This　is　a　recommended　method　to　improve　the　efficiency　of　prediction　calculations　and　reduce　system　response　time.　In　order　to　solve　the　problem　of　model　inaccuracy　caused　by　the　sparse　data　of　the　existing　collaborative　filtering　recommendation　scheme,　which　leads　to　the　inaccuracy　of　recommending　suitable　online　education　and　teaching　resources　to　different　users,　the　present　invention　uses　the　least　squares　collaborative　filtering　recommendation　algorithm　on　the　Spark　big　data　analysis　platform　Optimize　and　use,　and　then　use　parallel　methods　to　increase　the　amount　of　work　completed　per　unit　time　and　the　accuracy　of　recommendations,　and　solve　the　problem　of　inaccurate　recommendation　of　teaching　resources.