Distributed Denial of Service （DDoS） attacks remain one of the primary threats faced by modern networks due to their high stealthiness and polymorphism. With the development of the Internet of Things （IoT） and cloud computing， DDoS traffic exhibits characteristics such as suddenness， heterogeneity， and rapid evolution， posing challenges for traditional detection methods in complex attack identification and sample imbalance. To overcome these limitations， a DDoS detection method combining DeepInsight and Classical-to-Quantum Convolutional Neural Network （DI-C2Q-CNN） was proposed. First， DeepInsight technology was utilized to map multi-dimensional time-series network traffic data into structured grayscale images intelligently， and by reconstructing spatial relationships between features explicitly， the identifiability of attack patterns was enhanced. Second， a Classical-to-Quantum Convolutional Neural Network （C2Q-CNN） hybrid architecture was constructed， integrating classical Convolutional Neural Network （CNN） with quantum convolution modules. By leveraging the representational capability of quantum superposition states and high-dimensional feature mapping， the higher-order representation of image features and the ability to learn complex patterns were enhanced， thereby improving the capability to identify complex network attack patterns. Additionally， to address training bottlenecks caused by small samples and class imbalance， multiple transfer learning solutions were introduced to enhance model adaptability and robustness. Experimental results demonstrate that the proposed method outperforms mainstream models such as Support Vector Machine （SVM） and CNN-LSTM on the UNSW-NB15 dataset， even under extreme sample compression and class imbalance conditions， showing good performance and application potential in DDoS attack detection tasks.