Music　emotion　recognition　(MER),　a　sub-task　of　music　information　retrieval　(MIR),　has　developed　rapidly　in　recent　years.　However,　the　learning　of　affect-salient　features　remains　a　challenge.　In　this　paper,　we　propose　an　end-to-end　attention-based　deep　feature　fusion　(ADFF)　approach　for　MER.　Only　taking　log　Mel-spectrogram　as　input,　this　method　uses　adapted　VGGNet　as　spatial　feature　learning　module　(SFLM)　to　obtain　spatial　features　across　different　levels.　Then,　these　features　are　fed　into　squeeze-and-excitation　(SE)　attention-based　temporal　feature　learning　module　(TFLM)　to　get　multi-level　emotion-related　spatial-temporal　features　(ESTFs),　which　can　discriminate　emotions　well　in　the　final　emotion　space.　In　addition,　a　novel　data　processing　is　devised　to　cut　the　single-channel　input　into　multichannel　to　improve　calculative　efficiency　while　ensuring　the　quality　of　MER.　Experiments　show　that　our　proposed　method　achieves　10.43%　and　4.82%　relative　improvement　of　valence　and　arousal　respectively　on　the　R2　score　compared　to　the　state-of-the-art　model,　meanwhile,　performs　better　on　datasets　with　distinct　scales　and　in　multi-task　learning.　Copyright　©　2022　ISCA.