An auxiliary force control system for a power-assisted bicycle, wherein the auxiliary force control system is applied to a power-assisted bicycle having a motor and comprises: a sensing device mounted on the power-assisted bicycle to receive a riding torque (S 1) and a riding speed; characterized in that mobile computing device generating a tuning factor (Fm) via a first Artificial Neural Network model and a second Artificial Neural Network model, wherein personal data and historical riding data are input data of the first Artificial Neural Network model; a predicted grade outputted by the first Artificial Neural Network model, the personal data, and environment data are input data of the second Artificial Neural Network model; wherein the predicted grade is used to predict and indicate a riding skill of a rider; and the tuning factor (Fm) is output data of the second Artificial Neural Network model; wherein the tuning factor (Fm) is computed based on the rider's physical fitness corresponding to the historical riding data and the personal data and an on-site environment corresponding to the environment data; a first controller mounted on the power-assisted bicycle, signally connected to the mobile computing device to receive the tuning factor (Fm), and generating a final factor according to the tuning factor (Fm), a mode factor (Fn) of the power-assisted bicycle, and a gap-range factor (Gp); wherein the gap-range factor (Gp) is a value used to adjust the tuning factor (Fm); wherein the gap-range factor (Gp) is a value which is set according to the selected mode factor (Fn) and the personal status of the user, wherein the mode factor (Fn) corresponds to a selected power mode of the power-assisted bicycle and the mode factor (Fn) is a real number; and wherein the final factor is represented as the final factor is equal to the mode factor (Fn) plus the tuning factor (Fm) times the gap-range factor (Gp), which may be expressed as FF=Fn+Fm*Gp, and a second controller mounted on the power-assisted bicycle, signally connected to the sensing device, the first controller, and the motor, and comprising an intelligent module and a safety control module, wherein the intelligent module generates a parameter (Mt) of target output of the motor according to the final factor, the riding torque, and the riding speed; and the safety control module generates a motor driver current (Id) according to the parameter (Mt) of target output of the motor and outputs the motor driver current (Id) to the motor to drive the motor. a sensing device mounted on the power-assisted bicycle to receive a riding torque (S 1) and a riding speed; characterized in that mobile computing device generating a tuning factor (Fm) via a first Artificial Neural Network model and a second Artificial Neural Network model, wherein personal data and historical riding data are input data of the first Artificial Neural Network model; a predicted grade outputted by the first Artificial Neural Network model, the personal data, and environment data are input data of the second Artificial Neural Network model; wherein the predicted grade is used to predict and indicate a riding skill of a rider; and the tuning factor (Fm) is output data of the second Artificial Neural Network model; wherein the tuning factor (Fm) is computed based on the rider's physical fitness corresponding to the historical riding data and the personal data and an on-site environment corresponding to the environment data; a first controller mounted on the power-assisted bicycle, signally connected to the mobile computing device to receive the tuning factor (Fm), and generating a final factor according to the tuning factor (Fm), a mode factor (Fn) of the power-assisted bicycle, and a gap-range factor (Gp); wherein the gap-range factor (Gp) is a value used to adjust the tuning factor (Fm); wherein the gap-range factor (Gp) is a value which is set according to the selected mode factor (Fn) and the personal status of the user, wherein the mode factor (Fn) corresponds to a selected power mode of the power-assisted bicycle and the mode factor (Fn) is a real number; and wherein the final factor is represented as the final factor is equal to the mode factor (Fn) plus the tuning factor (Fm) times the gap-range factor (Gp), which may be expressed as FF=Fn+Fm*Gp, and a second controller mounted on the power-assisted bicycle, signally connected to the sensing device, the first controller, and the motor, and comprising an intelligent module and a safety control module, wherein the intelligent module generates a parameter (Mt) of target output of the motor according to the final factor, the riding torque, and the riding speed; and the safety control module generates a motor driver current (Id) according to the parameter (Mt) of target output of the motor and outputs the motor driver current (Id) to the motor to drive the motor.