As the traditional planar nearfiled acoustic holography (CPNAH) is an illposed problem, which cannot be solved completely with the wave number domain filtering or Tikhonov regularization method. A planar nearfield acoustic holography based on compressive sensing by using the smoothed l0 norm (SL0CSPNAH) was proposed in this work. According to the characteristics of the sound measurement on the holography plane, the orthogonal wavelet transform matrix is built by using the symlets8 wavelet function, which is used as a sparse basis for the particle normal velocity of the reconstructed plane. The Rayleigh first integral formula used in CPNAH is discretized for obtaining the measurement matrix conforming to the restricted isometry property (RIP) used in SL0-CS-PNAH, and the measurement matrix is used to sample the data in a proper compression ratio. The sparse vector least l0 norm optimization model is established in the constraint condition consisting of sensing matrix, measurement sound pressure of holographic plane, and sparse vector, which is solved by SL0-CS-PNAH, then the optimal sparse solutions is obtained, then the particle normal velocity is reconstructed by multiplying the optimal sparse solutions and sparse matrix. In simulation experiments, SL0-CS-PNAH is compared with CPNAH, orthogonal matching pursuit algorithm planar nearfield acoustic holography based on Compressive sensing (OMP-CS-PNAH), subspace pursuit algorithm planar nearfield acoustic holography based on Compressive sensing (SP-CS-PNAH) with measurement elements reducing from 64×64 to 32×64. The experimental results indicate that SL0-CS-PNAH has a better reconstruction precision and higher reconstruction efficiency under the condition of the same sampling rate and compression ratio.