Computed　tomography　(CT)-guided　thermal　ablation　is　an　emerging　treatment　method　for　lung　tumors.　Ablation　needle　path　planning　in　preoperative　diagnosis　is　of　critical　importance.　In　this　work,　we　proposed　an　automatic　needle　path-planning　method　for　thermal　lung　tumor　ablation.　First,　based　on　the　improved　cube　mapping　algorithm,　binary　classification　was　performed　on　the　surface　of　the　bounding　box　of　the　patient＇s　CT　image　to　obtain　a　feasible　puncture　area　that　satisfied　all　hard　constraints.　Then,　for　different　clinical　soft　constraint　conditions,　corresponding　grayscale　constraint　maps　were　generated,　respectively,　and　the　multi-objective　optimization　problem　was　solved　by　combining　Pareto　optimization　and　weighted　product　algorithms.　Finally,　several　optimal　puncture　paths　were　planned　within　the　feasible　puncture　area　obtained　for　the　clinicians　to　choose.　The　proposed　method　was　evaluated　with　18　tumors　of　varying　sizes　(482.79　mm3　to　9313.81　mm3)　and　the　automatically　planned　paths　were　compared　and　evaluated　with　manually　planned　puncture　paths　by　two　clinicians.　The　results　showed　that　over　82%　of　the　paths　(74　of　90)　were　considered　reasonable,　with　clinician　A　finding　the　automated　planning　path　superior　in　7　of　18　cases,　and　clinician　B　in　9　cases.　Additionally,　the　time　efficiency　of　the　algorithm　(35　s)　was　much　higher　than　that　of　manual　planning.　The　proposed　method　is　expected　to　aid　clinicians　in　preoperative　path　planning　for　thermal　ablation　of　lung　tumors.　By　providing　a　valuable　reference　for　the　puncture　path　during　preoperative　diagnosis,　it　may　reduce　the　clinicians＇　workload　and　enhance　the　objectivity　and　rationality　of　the　planning　process,　which　in　turn　improves　the　effectiveness　of　treatment.