This paper introduces Wav-KAN, an innovative neural network architecture that integrates wavelet functions into the Kolmogorov-Arnold Networks (KAN) framework. Wav-KAN aims to enhance interpretability and performance by leveraging the multiresolution analysis capabilities of wavelets. Traditional neural networks like multilayer perceptrons (MLPs) and Spl-KANs face challenges such as interpretability, training speed, robustness, and computational efficiency. Wav-KAN addresses these issues by using wavelet functions to capture both high-frequency and low-frequency components of input data efficiently. The paper compares Wav-KAN with Spl-KAN and MLPs, highlighting its advantages in terms of faster training speeds, improved accuracy, and enhanced robustness. Experimental results on the MNIST dataset demonstrate that Wav-KAN outperforms Spl-KAN and achieves superior accuracy with faster training times. The paper concludes by emphasizing the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields.This paper introduces Wav-KAN, an innovative neural network architecture that integrates wavelet functions into the Kolmogorov-Arnold Networks (KAN) framework. Wav-KAN aims to enhance interpretability and performance by leveraging the multiresolution analysis capabilities of wavelets. Traditional neural networks like multilayer perceptrons (MLPs) and Spl-KANs face challenges such as interpretability, training speed, robustness, and computational efficiency. Wav-KAN addresses these issues by using wavelet functions to capture both high-frequency and low-frequency components of input data efficiently. The paper compares Wav-KAN with Spl-KAN and MLPs, highlighting its advantages in terms of faster training speeds, improved accuracy, and enhanced robustness. Experimental results on the MNIST dataset demonstrate that Wav-KAN outperforms Spl-KAN and achieves superior accuracy with faster training times. The paper concludes by emphasizing the potential of Wav-KAN as a powerful tool for developing interpretable and high-performance neural networks, with applications spanning various fields.