In this work, an integrated neural radiance field model ensemble measurement system was developed to measure the ship shell plate molding accuracy detection. This is the first work that uses the neural radiance field model to solve the issue of ship shell plate molding accuracy detection. The neural radiance field model of Instant-NGP was deployed to reconstruct the 3D model of the ship shell plate, which significantly reduced the model training time and ensured high reconstruction accuracy. An image acquisition system was constructed by combining the calculation results, and measurement experiments were carried out on three different sizes of ship shell plates in the production process. The accuracy, efficiency, and completeness of the proposed method were evaluated. The results show that the point cloud data reconstruction of the plate is accomplished in about 2.5 min, and the average error is less than 0.2 mm, which meets the requirements of the ship shell plate molding measurement and reflects high accuracy. Based on the experimental results, the optimal image parameters of the data set for the model to reconstruct the ship shell plate are given. Compared with the wooden formwork method, the active binocular vision method, and the reconstruction method based on MVSNet, our approach has the advantages of high precision, high efficiency, and low cost, which proves flexibility and robustness.