Traffic flow prediction is crucial for efficient traffic management. It involves predicting vehicle movement patterns to reduce congestion and enhance traffic flow. However, the highly non-linear and complex patterns commonly observed in traffic flow pose significant challenges for this task. Current Graph Neural Network (GNN) models often construct shallow networks, which limits their ability to extract deeper spatio-temporal representations. Neural ordinary differential equations for traffic prediction address over-smoothing but require significant computational resources, leading to inefficiencies, and sometimes deeper networks may lead to poorer predictions for complex traffic information. In this study, we propose an Adaptive Decision spatio-temporal Neural Ordinary Differential Network, which can adaptively determine the number of layers of ODE according to the complexity of traffic information. It can solve the over-smoothing problem better, improving overall efficiency and prediction accuracy. In addition, traditional temporal convolution methods make it difficult to deal with complex and variable traffic time information with a large time span. Therefore, we introduce a multi-kernel temporal dynamic expansive convolution to handle the traffic time information. Multi-kernel temporal dynamic expansive convolution employs a dynamic dilation strategy, dynamically adjusting the network's receptive field across levels, effectively capturing temporal dependencies, and can better adapt to the changing time data of traffic information. Additionally, multi-kernel temporal dynamic expansive convolution integrates multi-scale convolution kernels, enabling the model to learn features across diverse temporal scales. We evaluated our proposed method on several real-world traffic datasets. Experimental results show that our method outperformed state-of-the-art benchmarks.