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Cultivating talents with innovation ability is both the goal of higher vocational colleges and their responsibility given by the era. Focusing on improving the practical knowledge level of students means that students should be exposed to nature, society and problems through practical activities, thus providing the maximum space for them to think, explore, discover and innovate. As for existing studies on cultivating innovative ability of students and improving their practical knowledge level, they mainly have focused on ability evaluation and analysis of influencing factors, and few of them has involved analysis of their practical knowledge level characteristics. Therefore, this paper studied the characteristics and transformation mechanism of their innovative practice achievements. A multi-level comprehensive evaluation system was constructed, which aimed to evaluate the practical knowledge level of those students. The input dimension of quantized data of indexes was reduced in accordance with the principal component analysis (PCA). Then the quantized data of the low-dimensional indexes were mined by hierarchical clustering, and different training subsets were generated to participate in the evaluation model training. Based on the idea of clustering-prediction-integration, Gated Recurrent Unit (GRU) model was used for nonlinear integration of the output results of two-way Long Short-Term Memory (LSTM) model, which further fit the nonlinear characteristics in the quantized data of all indexes. By introducing Bootstrap-Data Envelopment Analysis (DEA) method, the transformation rate of innovative practice achievements of the students was collected and calculated in order to obtain a small calculation error. Experimental results verified the effectiveness of the proposed calculation method of the model.

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