Passenger　evacuation　time　prediction　is　a　crucial　basis　for　subway　station　management　to　better　formulate　safety　control　measures.　It　becomes　possible　to　reasonably　plan　the　flow　of　passengers　within　the　station　and　configure　safety　devices　such　as　signage　and　sprinkler　systems　with　a　known　and　explicit　time　framework,　thereby　reducing　passenger　congestion　and　panic.　However,　methods　based　on　crowd　dynamics　simulation　require　a　significant　amount　of　time　and　effort　to　build　models,　and　the　rapidity　of　time　prediction　is　challenging　to　ensure.　Real　human　evacuation　experiments　involve　ethical,　safety　and　practical　operational　issues.　To　address　this　challenge,　an　evacuation　time　prediction　model　for　subway　passengers　is　established　based　on　the　CPA-SVR　machine　learning　method,　enhancing　the　speed　and　accuracy　of　prediction.　The　reliability　of　simulation　results　is　validated　by　comparing　observed　values　of　passenger　alighting　and　boarding　time　and　traffic　time　at　stairs　with　simulation　values　from　MassMotion　software.　Fourteen　factors　related　to　the　subway　station　structure,　passengers　and　train　status　are　selected　as　influence　factors　for　evacuation　time.　A　foundation　data　set　for　the　evacuation　time　prediction　model　is　obtained　through　179　evacuation　experiments　under　different　influence　factors　using　the　MassMotion　simulation　system　at　32　constructed　stations.　The　SHAP　interpretation　method　is　applied　to　identify　the　importance　of　influence　factors　in　the　experimental　results.　A　CPA-SVR　passenger　evacuation　time　prediction　model　is　established,　with　accuracy　concentrated　between　85%-100%,　based　on　training　and　validation　sets.　Further　testing　with　45　additional　sets　of　fresh　experimental　data　demonstrates　the　model＇s　strong　predictive　capability　for　new　data,　indicating　good　generalization　ability.　Finally,　a　focused　analysis　of　passenger　evacuation　behaviors　at　bottlenecks　such　as　stairs,　gates　and　exits　is　conducted,　accompanied　by　relevant　optimization　strategies.