The work aims to explore a hub form design method combining neural network algorithm and pixel theory, so as to realize the innovative design of product form under the premise of satisfying user's preference, ensure the market acceptance of the new form, and reduce the risk of product development. Firstly, the KJ method was used to establish the target hub sample library, and the Likert scale and the triangular fuzzy method were used to quantify the user's fuzzy preference value of the sample. Secondly, the ontology pixel in the pixel theory was introduced to extract and classify the morphological characteristic factors of the hub, construct the hub form ontology pixel space, and process the hub ontology pixel combination code. Then, the BP neural network algorithm was adopted to analyze the mapping relationship between the hub form ontology pixel combination code and the user's fuzzy preference value, which was used to predict and output the optimal ontology pixel combination scheme of the user's fuzzy preference value. Finally, the optimal ontology combination scheme was expanded to construct a metaphorical element space, and the optimal ontology pixel combination scheme was subject to innovative design of form by the rules of shape grammar. The test results of the BP neural model show that the MSE is 0.005 5, indicating that the model has good accuracy, and the MSE of the actual evaluation value and predicted value of the final four design schemes is 0.005 3, which verifies the effectiveness of the design method and can provide relevant designers with certain theoretical reference.