DNA　data　are　important　in　the　bioinformatic　domain.　To　extract　useful　information　from　the　enormous　collection　of　DNA　sequences,　DNA　clustering　is　often　adopted　to　efficiently　deal　with　DNA　data.　The　alignment-free　method　is　a　very　popular　way　of　creating　feature　vectors　from　DNA　sequences,　which　are　then　used　to　compare　DNA　similarities.　This　paper　proposes　a　wavelet-based　feature　vector　(WFV)　model,　which　is　also　an　alignment-free　method.　From　the　perspective　of　signal　processing,　a　DNA　sequence　is　a　sequence　of　digital　signals.　However,　most　traditional　alignment-free　models　only　extract　features　in　the　time　domain.　The　WFV　model　uses　discrete　wavelet　transform　to　adaptively　yield　feature　vectors　with　a　fixed　dimension　based　on　the　features　in　both　the　time　and　frequency　domains.　The　level　of　wavelet　transform　is　adjusted　according　to　the　length　of　the　DNA　sequence　rather　than　a　fixed　manually　set　value.　The　WFV　model　prefers　a　32-dimension　feature　vector,　which　greatly　promotes　system　performance.　We　compared　the　WFV　model　with　the　other　five　alignment-free　models,　i.e.,　k-tuple,　DMK,　TSM,　AMI,　and　CV,　on　several　large-scale　DNA　datasets　on　the　DNA　clustering　application　by　means　of　the　K-means　algorithm.　The　experimental　results　showed　that　the　WFV　model　outperformed　the　other　models　in　terms　of　both　the　clustering　results　and　the　running　time.