To address the problems of inefficient data loading and readout and poor flexibility of array collaboration in existing multi-memristor-array, a highly efficient and flexible multi-array interconnection architecture was proposed. The data loading strategy of the architecture supports data reuse in multiple weight mapping modes, reducing the need for off-chip data access; the readout network supports flexible combination of multiple processing units to achieve different scales of convolutional operations, as well as fast accumulation and readout of computation results, thus improving chip flexibility and overall computing power. Simulation experiments performed on the NeuroSim platform with running VGG-8 networks indicate a 146% increase in processing speed than that of the MAX² neural network accelerator, with only a 6% increase in area overhead.