The transformation of basic functions is one of the most commonly used techniques for seismic denois- ing，which employs sparse representation of seismic data in the transform domain． The choice of transform base functions has an influence on denoising results． We propose a learning- type overcomplete dictionary based on the K- singular value decomposition (K- SVD) algorithm． To construct the dictionary and use it for random seis- mic noise attenuation，we replace fixed transform base functions with an overcomplete redundancy function library． Owing to the adaptability to data characteristics，the learning- type dictionary describes essential data characteristics much better than conventional denoising methods． The sparsest representation of signals is ob- tained by the learning and training of seismic data． By comparing the same seismic data obtained using the learning- type overcomplete dictionary based on K- SVD and the data obtained using other denoising methods， we find that the learning- type overcomplete dictionary based on the K- SVD algorithm represents the seismic data more sparsely，effectively suppressing the random noise and improving the signal- to- noise ratio．