The　tensor　completion　issues　have　obtained　a　great　deal　of　attention　in　the　past　few　years.　However,　the　data　fidelity　part　minimizes　a　squared　loss　function,　which　may　be　inappropriate　for　the　case　of　noisy　one-bit　observations.　In　this　paper,　we　alleviate　the　mentioned　difficulty　by　drawing　on　the　experience　of　matrix　scenarios.　Based　on　the　convex　relation　to　L-1　norm　of　the　tensor　multi-rank,　we　propose　a　novel　optimization　model　trying　to　recover　the　underlying　tensor　in　case　of　one-bit　observations.　The　feasibility　of　this　model　is　proved　by　theoretical　derivations.　Furthermore,　an　alternating　direction　method　of　multipliers　based　algorithm　is　designed　to　find　the　solution.　The　numerical　experiments　demonstrate　the　effectiveness　of　our　method.