In order to solve the lack of reasonable arrangement of multi-modal fish disease knowledge， and at the same time reduce the redundant data in the knowledge distillation process， so as to deploy a recognition model with low storage， small samples， and high accuracy， this paper proposes a new method， named as FSFDAI-TMRD. In terms of multi-feature collaborative prediction， this paper focuses on improving the original multi-feature collaborative multi-feature prediction architecture of multi-tasks. Firstly， the finer-grained begin-middle-end-single （BMES） method is used instead of the rough labeling of the begin-inside-outside （BIO） method in the original work. Secondly， the formula for calculating the joint probability distribution of the original architecture is modified， so that the model can better recognize the nested noun entities. In terms of cross-modal multi-head distillation， this paper proposes to employ a cross-modal attention mechanism. Firstly， it calculates the multi-head relationship matrix after merging， splitting， and dot product， and secondly， it utilizes the relative entropy for knowledge distillation， so that the model can better align the intermediate features between heterogeneous teachers and students. Meanwhile， this paper also applies the biaffine attention and adversarial weight perturbation function to enhance the learning of multi-feature knowledge such as semantic phonology and word form word meaning. Compared with the mainstream model， the precision， recall and F₁ value of the FSFDAI-TMRD method are improved by 0.45%， 3.96% and 2.28%， respectively. The storage optimization ratio is improved by 3.01% and the model parameter size is reduced by 94.86%.