Within　the　domain　of　multimodal　communication,　the　compression　of　audio,　image,　and　video　information　is　well-established,　but　compressing　haptic　signals,　including　vibrotactile　signals,　remains　challenging.　Particularly　with　the　enhancement　of　haptic　signal　sampling　rate　and　degrees　of　freedom,　there　is　a　substantial　increase　in　data　volume.　While　existing　algorithms　have　made　progress　in　vibrotactile　codecs,　there　remains　significant　room　for　improvement　in　compression　ratios.　We　propose　an　innovative　Nbeats　Network-based　Vibrotactile　Codec　(NNVC)　that　leverages　the　statistical　characteristics　of　vibrotactile　data.　This　advanced　codec　integrates　the　Nbeats　network　for　precise　vibrotactile　prediction,　residual　quantization,　efficient　Run-Length　Encoding,　and　Huffman　coding.　The　algorithm　not　only　captures　the　intricate　details　of　vibrotactile　signals　but　also　ensures　high-efficiency　data　compression.　It　exhibits　robust　overall　performance　in　terms　of　Signal-to-Noise　Ratio　(SNR)　and　Peak　Signal-to-Noise　Ratio　(PSNR),　significantly　surpassing　the　state-of-the-art.