Wire and arc additive manufacturing(WAAM) is a promising method for directly manufacturing parts with complex shapes. However, the accuracy of the existing welding parameter planning methods would dramatically decrease when bead geometry changes dynamically due to the long-term dependence, strong coupling, and hysteresis properties of the WAAM process. To this end, a non-autoregressive(NAR) dynamic model is proposed to predict the bead geometry, and an adaptive model predictive control(aMPC) method is proposed to plan welding parameters to achieve high manufacturing accuracy. First, in the proposed dynamic model, the long-term dynamic characteristics of the WAAM process are modeled by a resample long short-term memory(Re-LSTM) network by considering the fluidity of the welding pool, which is the crucial factor of dynamic characteristics of the welding process. Second, in the proposed aMPC method, the strong coupling is addressed by high multi-objective performance, and the hysteresis is considered by a long control horizon. Thus, the aMPC method can reduce the control latency and improve the manufacturing accuracy for varying geometry beads. The proposed methods are validated by experiments, which indicate that the proposed methods are effective.