To solve the problem that traditional Wireless Body Area Network (WBAN) prediction model has low prediction accuracy, large computational complexity and high energy consumption, an adaptive cubic exponential smoothing algorithm based on penalty error matrix was proposed. Firstly, a lightweight prediction model was established between the sensing node and the routing node. Secondly, blanket search was used to optimize the parameters of the prediction model. Finally, penalty error matrix was used to further refine the parameters of the prediction model. The experimental results showed that compared with the ZigBee protocol, the proposed method saved about 12% energy in 1000 time slot range; compared with blanket search method, the prediction accuracy was improved by 3.306% by using penalty error matrix. The proposed algorithm can effectively reduce the computational complexity and further reduce the energy consumption of WBAN.