Intravoxel incoherent motion (IVIM) magnetic resonance imaging is a non-invasive technique, which can characterize the diffusion and perfusion of water molecules in biological tissues. Traditional IVIM parameters estimation methods are highly affected by the noise, and the parameter estimation is not effective. In order to accurately and quickly determine the diffusion and perfusion parameters in tissue regions, this study proposesd a one-dimensional dynamic convolutional neural network (DCNN) based on the dynamic convolutional module to estimate IVIM parameters. It takes into account the contextual information between the voxel signals and the contribution of b-values, to estimate IVIM parameters. The DCNN is compared with the traditional estimation method on the test simulation data and real acquisition images underwith different noise levels. The experimental results show that the proposed DCNN method can reduce the coefficient of variation, bias, and relative root mean square error of the IVIM parameters and, improve the parameter consistency and robustness, and have good visual quality at the same time.