Background　Nanoscale　connectomics,　which　aims　to　map　the　fine　connections　between　neurons　with　synaptic-level　detail,　has　attracted　increasing　attention　in　recent　years.　Currently,　the　automated　reconstruction　algorithms　in　electron　microscope　volumes　are　in　great　demand.　Most　existing　reconstruction　methodologies　for　cellular　and　subcellular　structures　are　independent,　and　exploring　the　inter-relationships　between　structures　will　contribute　to　image　analysis.　The　primary　goal　of　this　research　is　to　construct　a　joint　optimization　framework　to　improve　the　accuracy　and　efficiency　of　neural　structure　reconstruction　algorithms.　Results　In　this　investigation,　we　introduce　the　concept　of　connectivity　consensus　between　cellular　and　subcellular　structures　based　on　biological　domain　knowledge　for　neural　structure　agglomeration　problems.　We　propose　a　joint　graph　partitioning　model　for　solving　ultrastructural　and　neuronal　connections　to　overcome　the　limitations　of　connectivity　cues　at　different　levels.　The　advantage　of　the　optimization　model　is　the　simultaneous　reconstruction　of　multiple　structures　in　one　optimization　step.　The　experimental　results　on　several　public　datasets　demonstrate　that　the　joint　optimization　model　outperforms　existing　hierarchical　agglomeration　algorithms.　Conclusions　We　present　a　joint　optimization　model　by　connectivity　consensus　to　solve　the　neural　structure　agglomeration　problem　and　demonstrate　its　superiority　to　existing　methods.　The　intention　of　introducing　connectivity　consensus　between　different　structures　is　to　build　a　suitable　optimization　model　that　makes　the　reconstruction　goals　more　consistent　with　biological　plausible　and　domain　knowledge.　This　idea　can　inspire　other　researchers　to　optimize　existing　reconstruction　algorithms　and　other　areas　of　biological　data　analysis.