For image matching, the KAZE feature detection and description algorithm has demonstrated a number of advantages. However, the solution of Perona-Malik (P-M) model adopted by KAZE is not unique, and the weak edges of image are prone to be smoothed in scale spaces by nonlinear diffusion filter function when the feature points are detected. To overcome these problems, an improved KAZE feature detection and description algorithm for image matching (CKAZE) is proposed. Firstly, an adaptive diffusion filter is built based on the principle of KAZE and energy functional. Then, the solution uniqueness and the edge preserving capacity of the proposed adaptive diffusion filter function are studied during filtering process. Finally, the CKAZE is constructed and its performance is validated through image matching experiments on Mikolajczyk benchmark image dataset. The results demonstrate that the correct rates of feature matching through CKAZE is 4.555%, 2.138%, 0.656% and 1.981% higher, respectively, than those by KAZE for Gauss blurring, illumination, rotation zoom and visual transformation, which indicate that the accuracy of feature detection and description is improved by CKAZE.