This study proposed a one-dimensional convolutional encoder-decoder neural network called Dense-1D-U-Net based on the encoder-decoder structure with our design of dense blocks and added skip connections. Dense-1D-U-Net can adapt to various studies by modifying neural network parameters and changing weights initialization methods. Here, it is used in the SRSI method based on deep learning. End-to-end learning of the relationships between spectral interference fringes and real spectral phases utilizes input information without intermediate calculation, which is the advantage of deep learning. The fitting ability of the neural network is significantly improved using our design of dense blocks. The added skip connections can make good use of the primary information. The accuracy of spectral phase measurement using Dense-1D-U-Net is at least about one order of magnitude improved more than that of the traditional SRSI algorithm. It is verified that Dense-1D-U-Net, trained by simulated data, can calculate measured data (Fig. 10). However, laser pulses are more diverse in practice. In future studies, we will consider various conditions of laser pulses to enhance the dataset to adapt to the specific situation. The advantage of Dense-1D-U-Net is that it is robust and can adapt to different studies by training it on different datasets and initializing its weights in different ways. This neural network can be extended to ultrafast spectroscopy and related studies based on one-dimensional information.