In practical control problems with multiple conflicting objectives, multi-objective optimization (MOO) problems must be simultaneously addressed. To tackle these challenges, scholars have extensively studied multi-objective reinforcement learning (MORL) in recent years. However, due to the complexity of the system and the difficulty in determining preferences between objectives, complex continuous control processes involving MOO problems still require further research. In this study, an innovative goal-oriented MORL algorithm is proposed. The agent is better guided for optimization through adaptive thresholds and goal selection strategy. Additionally, the reward function is refined based on the chosen objective. To validate the approach, a comprehensive environment for the fermentation process is designed. Experimental results show that our proposed algorithm surpasses other benchmark algorithms in most performance metrics. Moreover, the Pareto solution set found by our algorithm is closer to the true Pareto frontier of fermentation problems. •A goal-oriented multiobjective reinforcement learning algorithm is proposed.•Adaptive threshold setting is implemented based on sparse Pareto solutions.•A novel goal expansion method is introduced for improved generalization.•Superior quality of Pareto solutions is achieved in a fermentation process.