We experimentally demonstrate the performance of a complex deep neural network (CDNN) equalizer in multi-band super-Nyquist carrier-less amplitude and phase (m-SCAP) modulation for underwater visible-light communications (UVLC). The in-phase and quadrature of the complex data after the match filter and down-sampling are combined as real number pairs and input to the CDNN, which outputs the real part and the imaginary part of the equalized complex data. We compare the different performances of three pulse shapings [better-than-Nyquist pulse shaping (BTN), square-root raised cosine (SRRC), and Xia] utilized in the m-SCAP UVLC system based on the CDNN. We demonstrate that the CDNN equalizer can outperform the traditional equalizer based on the Volterra series and least-mean-square algorithm. The experiments show that the low-roll-off BTN performs best, and the high-roll-off SRRC performs best in the m-SCAP system. In our experiment, the bit-error rate (BER) of the BTN system is 2.6  ×  ^{10  −  4}, the BER of the SRRC is 3.6  ×  10^−  4, and the BER of the Xia system is 7.3  ×  10^−  4 when the spectral efficiency is 3.76  bit  /  s  /  Hz and the signal peak-to-peak voltage (Vpp) is 0.9 V.