Fetal heart rate (FHR) abnormality detection is a critical task in perinatal monitoring, providing essential support for fetal health assessment and delivery decision-making. Existing methods are mostly limited to binary or ternary classification, lacking the ability to identify more complex abnormal patterns. This paper proposes a fine-grained classification framework based on a Time Series 3D Convolutional Neural Network (TS3DCNN), capable of recognizing seven FHR patterns: acceleration, prolonged acceleration, early deceleration, late deceleration, variable deceleration, prolonged deceleration, and background. The framework employs Fast Fourier Transform (FFT) to extract periodic features and introduces a Multiscale Convolutional Unit (MCU) to enhance both local and global feature representations. The experimental results show that TS3DCNN achieves an accuracy of 65.59% in the seven-class classification task, significantly outperforming existing models, especially in detecting rare categories such as prolonged deceleration. These findings demonstrate the effectiveness of the proposed method in fine-grained FHR classification and its potential for real-time fetal monitoring and early risk prediction.