Point　clouds　are　essential　3D　data　representations　utilized　across　various　disciplines,　often　requiring　point　cloud　completion　methods　to　address　inherent　incompleteness.　Existing　completion　methods　like　SnowflakeNet　only　consider　local　attention,　lacking　global　information　of　the　complete　shape,　and　tend　to　suffer　from　overfitting　as　the　model　depth　increases.　To　address　these　issues,　we　introduced　self-positioning　point-based　attention　to　better　capture　complete　global　contextual　features　and　designed　a　Channel　Attention　module　for　adaptive　feature　adjustment　within　the　global　vector.　Additionally,　we　implemented　a　vector　attention　grouping　strategy　in　both　the　skip-transformer　and　self-positioning　point-based　attention　to　mitigate　overfitting,　improving　parameter　efficiency　and　generalization.　We　evaluated　our　method　on　the　PCN　dataset　as　well　as　the　ShapeNet55/34　datasets.　The　experimental　results　show　that　our　method　achieved　an　average　CD-L1　of　7.09　and　average　CD-L2　scores　of　8.0,　7.8,　and　14.4　on　the　PCN,　ShapeNet55,　ShapeNet34,　and　ShapeNet-unseen21　benchmarks,　respectively.　Compared　to　SnowflakeNet,　we　improved　the　average　CD　by　1.6%,　3.6%,　3.7%,　and　4.6%　on　the　corresponding　benchmarks,　while　also　reducing　complexity　and　computational　costs　and　accelerating　training　and　inference　speeds.　Compared　to　other　existing　point　cloud　completion　networks,　our　method　also　achieves　competitive　results.