The goal of assembly blocks is to select blocks from pre-trained neural network (NN) models and combine them into a new NN for a different dataset. By reusing the weights of these blocks, training the NN with the new dataset becomes cost-effective. To achieve this goal, we propose an end-to-end differentiable neural network called PA-DNN. PA-DNN consists of two modules: a partition NN module and an assembly NN module. For the new dataset, the partition NN module divides existing pre-trained NN models into blocks. The assembly NN module then selects some of these blocks and combines them into a new NN using a stitching component. To train PA-DNN, we design a score function that evaluates the performance of each new NN generated by PA-DNN. The evaluated value is used to train the partition NN module. Additionally, two loss functions are created to train the assembly NN module and the stitching component in the new NN, respectively. After the training process, PA-DNN infers a new NN, and only the stitching component of the NN is fine-tuned with the new dataset. Experiments show that, compared to manual models, neural architecture search, and the assembly model DeRy, PA-DNN can generate a more accurate and lightweight NN with lower training costs.