Time-frequency　analysis　(TFA)　is　a　powerful　tool　for　describing　time-frequency　(TF)　features　of　seismic　data,　such　as　short-time　Fourier　transform　(STFT)　and　S-transform　(ST).　Recently,　sparse　TFA　(STFA)　is　proposed　for　enhancing　TF　readability　of　commonly　used　TFA　tools.　However,　STFA　is　often　solved　via　an　optimal　inverse　problem　with　a　prior　regularization　term,　which　is　difficult　to　set　in　practice,　where　the　key　regularization　parameters　are　sensitive　to　noise.　Moreover,　it　often　takes　expensive　calculation　time,　especially　for　3-D　field　data　application.　We　build　a　deep　learning　(DL)-based　workflow　for　implementing　STFA　to　obtain　sparse　TF　(STF)　spectra,　termed　the　STF　network　with　transfer　learning　(STFNTL).　We　first　adopt　a　Marmousi　II　reflectivity　model　and　Ricker　wavelets　with　different　dominant　frequencies　to　generate　synthetic　training　dataset.　Then,　we　adopt　a　simplified　STFA　method　with　optimized　parameters　to　generate　synthetic　training　labels,　i.e.,　sparse　TF　spectra.　Afterward,　we　propose　the　STF　network　(STFN)　based　on　a　simplified　Unet　model,　which　is　trained　using　synthetic　training　data　and　corresponding　STF　labels.　Moreover,　to　enhance　the　generalization　of　STFN,　we　introduce　an　adaptive　transfer　learning　(TL)　strategy　based　on　small　samples　of　field　data　and　their　corresponding　STF　labels.　Finally,　synthetic　and　field　data　are　utilized　to　illustrate　the　effectiveness　and　generalization　ability　of　our　proposed　model.