Heterogeneous　information　network　(HIN)　is　a　kind　of　large-scale　network　which　contains　different　types　of　objects　and　complex　links.　It　is　distinguished　from　a　homogenous　network　for　its　heterogeneity　of　objects　represented　as　nodes　and　complexity　of　links,　which　also　makes　the　object　classification　more　difficult.　A　meta-path　can　denote　the　relationship　between　nodes　in　HINs,　and　the　path　information　can　be　enriched　by　extracting　jump-paths.　Based　on　this　idea,　the　problem　of　data　sparseness　can　be　alleviated　effectively.　As　multiple　meta-paths　represent　different　semantics,　we　propose　an　active　weight　learning　method　for　each　type,　which　aims　to　maximize　the　weight　of　meta-path　with　strong　correlation　and　lower　the　weight　if　the　correlation　is　weak.　The　feature　matrix　based　on　the　meta-path　is　constructed　and　the　Random　Forest　classifier　is　trained　to　implement　the　node　classification　in　HIN5.　The　experimental　results　show　that　our　method　achieves　better　performance　in　the　complex　network　by　using　the　fewer　labeled　data.　The　active　learning　strategy　is　effective　for　identifying　objects　to　label　for　training.　(C)　2019　Elsevier　Ltd.　All　rights　reserved.