One-shot　video-based　person　re-identification　exploits　the　unlabeled　data　by　using　a　single-labeled　sample　for　each　individual　to　train　a　model　and　to　reduce　the　need　for　laborious　labeling.　Although　recent　works　focusing　on　this　task　have　made　some　achievements,　most　state-of-the-art　models　are　vulnerable　to　misalignment,　pose　variation　and　corrupted　frames.　To　address　these　challenges,　we　propose　a　one-shot　video-based　person　re-identification　model　based　on　pose-guided　spatial　alignment　and　KFS.　First,　a　spatial　transformer　sub-network　trained　using　pose-guided　regression　is　employed　to　perform　the　spatial　alignment.　Second,　we　propose　a　novel　training　strategy　based　on　KFS.　Key　frames　with　abruptly　changing　poses　are　deliberately　identified　and　selected　to　make　the　network　adaptive　to　pose　variation.　Finally,　we　propose　a　frame　feature　pooling　method　by　incorporating　long　short-term　memory　with　an　attention　mechanism　to　reduce　the　influence　of　corrupted　frames.　Comprehensive　experiments　are　presented　based　on　the　MARS　and　DukeMTMC-VideoReID　datasets.　The　mAP　values　for　these　datasets　reach　46.5%　and　68.4%,　respectively,　demonstrating　that　the　proposed　model　achieves　significant　improvements　over　state-of-the-art　one-shot　person　re-identification　methods.