Time series classification is vital across many fields, despite complex data making precise classification challenging. Deep learning models have advanced, but interpretable models still dominate practical applications. Using time series Shapelet features reduces data and improves model interpretability, but extraction requires significant computing power and can lose some time series information. To address this, we apply an improved genetic algorithm for efficient Shapelet extraction. Additionally, we introduce discrete Fourier transform for frequency domain feature description, aiming to capture periodic patterns lossless. We propose a time series classification enhancement method combining Shapelet features and frequency domain characterization (E-STAR), providing a fast way to obtain Shapelets and enhancing recognition of global time series information. E-STAR outperforms four Shapelet-based algorithms on 32 UCR datasets, improving accuracy and interpretability.