A quantum multi-classification network based on parameterized quantum circuits is proposed for handwritten digit image classification tasks. Classical 28 \(\times \) 28 pixel images are downsampled to 4 \(\times \) 4 resolution, and the resulting 16 classical data points encoded into probability amplitudes of 4-qubit quantum states. These quantum states are input into a quantum state preparation circuit. The parameterized quantum circuit, composed of rotation and entanglement layers with trainable parameters, is designed to process the input quantum states. Projective measurements are performed on the output quantum states to obtain measurement vectors, which are transformed into output value vectors corresponding to One-hot labels through the Softmax function. The quantum 4-class classification network is taken as an example to introduce in detail. We also extended to 5–10 classification tasks.