Ubiquitous artificial intelligence (AI) applications call for increasing computing power and large communication bandwidth, which are challenging to meet given the diminishing benefits from traditional planar CMOS scaling. Three-dimension (3D) integration of integrated circuits (IC) promises to achieve further performance improvements at reduced power and smaller footprint. Here, we report a 3D array of 8 layers of field-programmable ferroelectric diode (FPD) for binary convolutional neural network (BCNN) in energy-efficient AI applications. The convolutional kernels are duplicated and stored in the pillars in 3D FPD array to providing high computation parallelism. An AND-based logic using FPD is proposed to realize the XNOR function in BCNN. As a proof-of-concept demonstration, a BCNN is constructed based on the 3D FPD array for MNIST test and qualified to present the advantages of our proposal in 28nm logic process design kit.