To　address　the　problem　of　form-finding　in　complex　symmetric　tensegrity　structures,　a　topology　form-finding　algorithm　based　on　a　parallel　component　coding　strategy　(PCC)　is　proposed.　Initially,　parallel　components　are　categorized　into　groups　based　on　the　base　structure　model.　By　integrating　tensegrity　structure　morphological　analysis　theory,　the　spatial　morphology　of　the　structure　is　established　through　the　splicing　and　recombining　of　components　from　different　groups　while　accounting　for　constraints　such　as　element　collision,　geometric　stability,　and　the　zero-bar　phenomenon.　Subsequently,　metrics,　including　the　symmetry　index,　pressure　bars　count　ratio,　and　internal　force　uniformity,　are　employed　to　evaluate　and　analyze　various　structural　topological　forms.　Using　12-node　and　18-node　spatial　tensegrity　structures　as　examples,　a　tensegrity　structure　with　high　topological　symmetry　is　effectively　obtained　and　screened　according　to　different　group　numbers,　thereby　fully　verifying　the　feasibility　and　accuracy　of　the　algorithm.　The　Random　Forest　Classification　(RF)　model　was　introduced　to　form　the　PCC-RF　algorithm.　By　comparing　the　computational　efficiency　of　PCC　and　PCC-RF　in　the　form-finding　process　of　tensegrity　structures,　the　results　indicate　that　PCC-RF　significantly　reduces　computational　time　and　provides　a　new　efficient　approach　for　exploring　complex　and　highly　symmetric　tensegrity　structures.