When　employing　penetration　ammunition　to　strike　multi-story　buildings,the　detection　methods　using　acceleration　sensors　suffer　from　signal　aliasing,while　magnetic　detection　methods　are　susceptible　to　interference　from　ferromagnetic　materials,thereby　posing　challenges　in　accurately　determining　the　number　of　layers.To　address　this　issue,this　research　proposes　a　layer　counting　method　for　penetration　fuze　that　incorporates　multi-source　information　fusion,utilizing　both　the　temporal　convolutional　network(TCN)and　the　long　short-term　memory(LSTM)recurrent　network.By　leveraging　the　strengths　of　these　two　network　structures,the　method　extracts　temporal　and　high-dimensional　features　from　the　multi-source　physical　field　during　the　penetration　process,establishing　a　relationship　between　the　multi-source　physical　field　and　the　distance　between　the　fuze　and　the　target　plate.A　simulation　model　is　developed　to　simulate　the　overload　and　magnetic　field　of　a　projectile　penetrating　multiple　layers　of　target　plates,capturing　the　multi-source　physical　field　signals　and　their　patterns　during　the　penetration　process.The　analysis　reveals　that　the　proposed　multi-source　fusion　layer　counting　method　reduces　errors　by　60％and　50％compared　to　single　overload　layer　counting　and　single　magnetic　anomaly　signal　layer　counting,respectively.The　model＇s　predictive　performance　is　evaluated　under　various　operating　con-ditions,including　different　ratios　of　added　noise　to　random　sample　positions,penetration　speeds,and　spacing　between　target　plates.The　maximum　errors　in　fuze　penetration　time　predicted　by　the　three　modes　are　0.08　ms,0.12　ms,and　0.16　ms,respectively,confirming　the　robustness　of　the　proposed　model.Moreover,the　model＇s　predictions　indicate　that　the　fitting　degree　for　large　interlayer　spacings　is　superior　to　that　for　small　interlayer　spacings　due　to　the　influence　of　stress　waves.