The　Conceptor　network　is　a　new　framework　of　reservoir　computing　(RC),　in　addition　to　the　features　of　easy　training,　global　convergence,　it　can　online　learn　new　classes　of　input　patterns　without　complete　re-learning　from　all　the　training　data.　The　conventional　connection　topology　and　weights　of　the　hidden　layer　(reservoir)　of　RC　are　initialized　randomly,　and　are　fixed　to　be　no　longer　fine-tuned　after　initialization.　However,　it　has　been　demonstrated　that　the　reservoir　connection　of　RC　plays　an　important　role　in　the　computational　performance　of　RC.　Therefore,　in　this　paper,　we　optimize　the　Conceptor＇s　reservoir　connection　and　propose　a　phase　space　reconstruction　(PSR)　-based　reservoir　generation　method.　We　tested　the　generation　method　on　time　series　prediction　task,　and　the　experiment　results　showed　that　the　proposed　PSR-based　method　can　improve　the　prediction　accuracy　of　Conceptor　networks.　Further,　we　compared　the　PSR-based　Conceptor　with　two　Conceptor　networks　of　other　typical　reservoir　topologies　(random　connected,　cortex-like　connected),　and　found　that　all　of　their　prediction　accuracy　showed　a　nonlinear　decline　trend　with　increasing　storage　load,　but　in　comparison,　our　proposed　PSR-based　method　has　the　best　accuracy　under　different　storage　loads.