There　has　been　increasing　interest　in　the　analysis　and　mining　of　Heterogeneous　Information　Networks　(HINs)　and　the　classification　of　their　components　in　recent　years.　However,　there　are　multiple　challenges　associated　with　distinguishing　different　types　of　objects　in　HINs　in　real-world　applications.　In　this　paper,　a　novel　framework　is　proposed　for　the　weighted　Meta　graph-based　Classification　of　Heterogeneous　Information　Networks　(MCHIN)　to　address　these　challenges.　The　proposed　framework　has　several　appealing　properties.　In　contrast　to　other　proposed　approaches,　MCHIN　can　fully　compute　the　weights　of　different　meta　graphs　and　mine　the　latent　structural　features　of　different　nodes　by　using　these　weighted　meta　graphs.　Moreover,　MCHIN　significantly　enlarges　the　training　sets　by　introducing　the　concept　of　Extension　Meta　Graphs　in　HINs.　The　extension　meta　graphs　are　used　to　augment　the　semantic　relationship　among　the　source　objects.　Finally,　based　on　the　ranking　distribution　of　objects,　MCHIN　groups　the　objects　into　pre-specified　classes.　We　verify　the　performance　of　MCHIN　on　three　real-world　datasets.　As　is　shown　and　discussed　in　the　results　section,　the　proposed　framework　can　effectively　outperform　the　baselines　algorithms.