This study presents a voiceprint recognition framework based on an adaptive feature extraction method that improves the traditional Mel scale transformation. By replacing fixed Mel-scale filters with adaptive ones, the method adjusts frequency parameters to better capture important speech features. These features are then classified using a ResCNN-Attention Network, enabling reliable recognition in different environments. The adaptive transformation makes feature representation more effective, showing promise for advancing voiceprint recognition.