Photoacoustic tomography is a noninvasive medical imaging technology. It has many advantages over other imaging methods and provides a new imaging idea for early diagnosis of tumors. The analysis and denoising of photoacoustic signals can improve the signal-to-noise ratio (SNR) and imaging quality of imaging systems. Thus, an intelligent denoising algorithm for photoacoustic signals is proposed. First, the adaptive white-noise complete set empirical mode decomposition was used to decompose the photoacoustic signal. Second, the wavelet threshold denoising method was used to complete the high-frequency denoising of the specific mode photoacoustic signal. Finally, the preprocessed photoacoustic signal was reconstructed sparsely by K-singular value decomposition to realize the intelligent denoising of photoacoustic signals. The simulation and experimental results show that when compared with other denoising algorithms, the SNR and root mean square error (RMSE) of the image obtained by the proposed algorithm improved. It can effectively remove the noise and artifacts in the three-dimensional tumor phantom photoacoustic reconstruction image and retain the edge information of the image. The proposed intelligent denoising algorithm can adaptively complete denoising according to the characteristics of noisy photoacoustic signals and achieve better denoising effects, which can be used as an auxiliary method for photoacoustic imaging.