The　triaxial　accelerometer　is　one　of　the　most　important　sensors　for　human　activity　recognition　(HAR).　It　has　been　observed　that　the　relations　between　the　axes　of　a　triaxial　accelerometer　plays　a　significant　role　in　improving　the　accuracy　of　activity　recognition.　However,　the　existing　research　rarely　focuses　on　these　relations,　but　rather　on　the　fusion　of　multiple　sensors.　In　this　paper,　we　propose　a　data　fusion-based　convolutional　neural　network　(CNN)　approach　to　effectively　use　the　relations　between　the　axes.　We　design　a　single-channel　data　fusion　method　and　multichannel　data　fusion　method　in　consideration　of　the　diversified　formats　of　sensor　data.　After　obtaining　the　fused　data,　a　CNN　is　used　to　extract　the　features　and　perform　classification.　The　experiments　show　that　the　proposed　approach　has　an　advantage　over　the　CNN　in　accuracy.　Moreover,　the　single-channel　model　achieves　an　accuracy　of　98.83%　with　the　WISDM　dataset,　which　is　higher　than　that　of　state-of-the-art　methods.