The　Conceptor　network　is　a　newly　proposed　reservoir　computing　(RC)　model,　which　outperforms　traditional　classifiers,　which　can　fail　to　model　new　classes　of　data　for　a　supervised　learning　task.　However,　the　reservoir　structure　design　for　the　Conceptor　is　single,　involving　just　a　traditional　random　network,　which　has　strong　coupling　between　nodes　and　limits　computing　ability.　This　study　focused　on　the　reservoir　topology　design　problem,　and　we　propose　a　complex　network　Conceptor-based　phase　space　reconstruction　of　time　series.　Several　dynamical　systems　were　chosen　to　build　complex　networks　using　a　phase　space　reconstruction　algorithm.　The　experiment　results　obtained　using　a　mix　of　two　irrational-period　sines　showed　that　the　proposed　phase　space　reconstruction　reservoir　topologies　with　the　appropriate　values　of　threshold　provide　Conceptors　with　extra　reconstruction　precision.　Among　them,　the　phase　space　reconstruction　reservoir-based　Lorenz　system　shows　the　best　performance.　Further　experiments　also　identified　the　appropriate　values　of　threshold　of　the　phase　space　reconstruction　method　required　to　obtain　optimal　performance.　The　precision　showed　a　non-linear　decline　with　increase　in　memory　load,　and　the　proposed　Lorenz　phase　space　reconstruction　reservoir　maintained　its　advantages　under　different　memory　loads.