Kinesthetic and tactile information can represent the physical states of objects, encompassing roughness, stiffness, motion, force, and other attributes. The introduction of these can enhance imprecise recognition that relies solely on visual information in cases of light disturbance, occlusion and camouflage. Nevertheless, this task is still challenging due to the heterogeneity among visual, tactile and kinesthetic data. To address this issue, this paper delves into the alignment of heterogeneous data dimensions, the fusion of heterogeneous data features, and the optimization of learning rates for multi-source heterogeneous sensor learning models. Consequently, an effective Visual-Kinesthetic-Tactile Information Fusion (VikitaFusion) network is proposed, which comprises: 1) heterogeneous data extractors that align visual images with tactile and kinesthetic data through image-to-sequence projection; 2) a visual-kinesthetic-tactile Transformer-based domain fusion that mimics human multi-sensory fusion perception through a feature-level fusion block and dynamic fusion blocks; 3) a Periodic Triangulation Learning Rate (PTLR) method aimed at optimizing the learning rate for performance enhancement in multi-source heterogeneous sensor learning models. Extensive experiments demonstrate that VikitaFusion outperforms current state-of-the-art methods with higher recognition accuracy and a lower parameter size. Source code will be made publicly available after the peer review process.