The inverse heat transfer problem (IHTP) is a central task for estimating parameters in heat transfer. It is ill-posedness that is characterised by instability and non-uniqueness of the solution. Recently, novel algorithms using deep learning and neural networks for application of various sparse data in the inverse heat transfer problem. In order to overcome the optimization problem of input nodes under sparse data, we try to use the overall data of the target as the basis for inversion. In this work, we used an improved convolutional neural network (CNN) to estimate multi-parameters in the inverse heat transfer problem. Computational fluid dynamics (CFD) and deep learning are fused to provide datasets for training of the proposed model. The proposed model was verified by experiments with a cubic cavity. Additionally, the improved CNN model was used to predict the different parameters of the more complex armored vehicle model. The results showed that the model has good prediction accuracy for estimating multi-parameters on different datasets. These attempts of introducing convolutional neural network to the IHTP in the present study were successful and it was significant for the study of the inverse heat transfer problem of estimating multi-parameters.