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The purpose of this study was to ensure the compliance and safety of a robot’s movements during interactions with the external environment. This paper proposes a control strategy for learning variable impedance characteristics from multiple sets of demonstration trajectories. This strategy can adapt to the control of different joints by adjusting the parameters of the variable impedance control policy. Firstly, multiple sets of demonstration trajectories are aligned on the time axis using Dynamic Time Warping. Subsequently, the variance obtained through Gaussian Mixture Regression and a variable impedance strategy based on an improved Softplus function are employed to represent the variance as the variable impedance characteristic of the robotic arm, thereby enabling variable impedance control for the robotic arm. The experiments conducted on a self-designed robotic arm demonstrate that, compared to other variable impedance methods, the motion accuracy of the trajectories of joints 1 to 4 improved by 57.23%, 3.66%, 5.36%, and 20.16%, respectively. Additionally, a stiffness-variable segmented generalization method based on Dynamic Movement Primitive is proposed to achieve variable impedance control in various task environments. This strategy fulfills the requirements for compliance and safety during robot interactions.