As the penetration rate of wind turbines increases, the power system inertia level decreases year by year. At the same time, the frequency response has partition characteristics, and it is more flexible and accurate to evaluate the power system inertia in terms of region. Therefore, a dynamic partition inertia estimation method is presented based on one-dimensional squeeze and excitation residual neural network （1D-SE-ResNet）. Firstly, the trend and value approximation of frequency distance are computed, and the k-means clustering method is used to dynamically partition the system and the number of partitions is determined by the S-C metric. Then, the 1D-ResNet is improved by adding the squeeze and excitation module, which enables it to provide weights for each channel to enhance the network performance. The regional cluster centre node frequency and frequency change rate data under different inertia levels and load perturbations of the system are collected as one-dimensional feature inputs, and the regional effective inertia is the output, training the network to achieve regional inertia estimation. Finally, simulations are carried out on the IEEE 39-node and IEEE 118- node systems containing wind power. The results show that the trained 1D-SE-ResNet can achieve accurate evaluation of regional inertia based on dynamic partitioning.