Pattern recognition of maneuvering target is challenging due to its rapidly changing patterns and uncertainties. Short-term trajectories are essential for capturing dynamic motion changes. However, existing deep-learning based methods rely on long-term trajectories to extract more comprehensive information, making them less effective with limited data. In this paper, we focus on recognizing short-term trajectories and propose a multilevel integrated framework that narrows the latent search space. Our approach employs a two-level classification with meta-learning (MetaMLIR) to recognize target motion patterns by learning multivariate features from noisy observations. Specifically, the first-level classification generates preliminary scores derived from multiple heterogeneous classifiers with a balanced integration strategy. The second-level classification uses logistic regression with meta-learning to share knowledge from the first-level outputs and provide the final classification result for each motion behavior. Additionally, we develop a target motion trajectory dataset (namely, TMT) to conduct comprehensive performance evaluation. Experimental results on our self-developed TMT dataset and the real-world NCLT dataset demonstrate that the proposed method achieves enhanced motion-pattern recognition performance across both non-pattern-switching and pattern-switching scenarios.