<p>This paper investigates the distributed consensus problem for strict-feedback nonlinear multi-agent systems featuring adjustable prescribed performance subject to completely unknown nonlinearities. A novel distributed consensus control scheme is proposed based on a data-driven fuzzy learning (DDFL) predictor. Firstly, fuzzy logic systems are utilized to approximate unknown nonlinearities. Next, a DDFL predictor is designed where the instantaneous data along with historical data are used simultaneously for parameter adaptation. By using the proposed predictor, the prediction error is employed in the design of the DDFL adaptation law, which significantly improves the transient performance. To tackle asymmetric error constraints, an adjustable prescribed performance function is introduced into the controller design, enabling arbitrary adjustable convergence rate of the synchronization error while guaranteeing the boundedness of it. Finally, an example of unmanned surface vehicle course control validates the efficacy of the proposed approach.</p>