Many　well-established　models　exist　for　predicting　the　dispersion　of　radioactive　particles　that　will　be　generated　in　the　surrounding　environment　after　a　nuclear　weapon　explosion.　However,　without　exception,　almost　all　models　rely　on　accurate　source　term　parameters,　such　as　DELFIC,　DNAF-1,　and　so　on.　Unlike　nuclear　experiments,　accurate　source　term　parameters　are　often　not　available　once　a　nuclear　weapon　is　used　in　a　real　nuclear　strike.　To　address　the　problems　of　unclear　source　term　parameters　and　meteorological　conditions　during　nuclear　weapon　explosions　and　the　complexity　of　the　identification　process,　this　article　proposes　a　nuclear　weapon　source　term　parameter　identification　method　based　on　a　genetic　algorithm　(GA)　and　a　particle　swarm　optimization　algorithm　(PSO)　by　combining　real-time　monitoring　data.　The　results　show　that　both　the　PSO　and　the　GA　are　able　to　identify　the　source　term　parameters　satisfactorily　after　optimization,　and　the　prediction　accuracy　of　their　main　source　term　parameters　is　above　98%.　When　the　maximum　number　of　iterations　and　population　size　of　the　PSO　and　GA　were　the　same,　the　running　time　and　optimization　accuracy　of　the　PSO　were　better　than　those　of　the　GA.　This　study　enriches　the　theory　and　method　of　radioactive　particle　dispersion　prediction　after　a　nuclear　weapon　explosion　and　is　of　great　significance　to　the　study　of　environmental　radioactive　particles.