# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 4 Experimental Results and Analysis
### Experimental Result and Analysis  
The experimental results are shown in Table 3. Among them, the labels of IFLYTEK's long text dataset are not publicly available, and the results are obtained from the Chinese Language Understanding Evaluation Benchmark (CLUE) assessment, which only provides the accuracy value. From the experimental results, we can see that the method we proposed, namely, the KGBGCN model, has the best ACC and F1 values on all three datasets, which proves the effectiveness of KGBGCN. A comparison of the results of the experiments is shown in Figure 4.  
According to the experimental results, it can be observed that the classification effect of machine learning methods is generally lower than that of deep learning, which may be due to its smaller number of features and the larger data size of the dataset we tested, so the classification effect is relatively lower. We can observe that the KNN model has the worst effect. Its core idea is that for the point to be judged, it finds a few data points closest to it and decides the type of the point to be judged according to their types. It is characterized by completely following the data, and there is no mathematical model to speak of. The core idea of Bayesian is to calculate the type of the point to be judged according to the conditional probability, which is a relatively easy-to-understand model and is still used by spam filters, but the results of dealing with high-dimensional data are not satisfactory. The core idea of the SVM model is to find the interface between different categories so that the samples of the two categories fall on both sides of the face and as far as possible from the interface. The earliest SVM was flat and very limited. SVM has excellent performance on many data sets. SVM is relatively resistant to attack due to its nature of trying to maintain the distance between samples. However, SVM is still essentially \"shallow learning\", as it can only classify linearly without a kernel function, and with a kernel function, it is essentially mapping a linearly indistinguishable problem to a distinguishable one, which is still linear in nature. The test results of random forest on the Fudan Chinese dataset are better than the above machine learning methods, which shows that the random forest \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **IFLYTEK (\%)** & \multicolumn{2}{c}{**THUCNews (\%)**} & \multicolumn{2}{c}{**Fudan (\%)**} \\ \hline Model & ACC & ACC & F1 & ACC & F1 \\ KNN & - & 75.31 & 74.37 & 74.18 & 72.69 \\ NB & - & 76.82 & 75.61 & 78.11 & 76.89 \\ Linear SVM & - & 78.04 & 76.86 & 78.37 & 77.48 \\ Random Forest & - & - & - & 78.79 & 80.07 \\ LSTM & - & 86.64 & 86.59 & 84.38 & - \\ BiLSTM & - & 87.02 & 87.02 & - & 82.57 \\ TextCNN & 53.94 & 86.53 & 82.74 & 86.21 & 80.02 \\ BERT & 55.73 & 89.04 & 84.89 & 89.63 & 82.79 \\ TextGCN & 56.35 & 87.78 & 82.80 & 86.53 & 80.42 \\ BertGCN & 58.78 & 91.53 & 84.53 & 91.04 & 86.03 \\ KGBGCN(Ours) & 60.65 & 92.25 & 86.83 & 91.99 & 86.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and F1 values of different datasets. method has some advantages in the multi-classification problem. Compared with machine learning methods, deep learning models are more complex, resulting in a larger system of models and algorithms and, thus, greater progress in functionality. Both LSTM and BiLSTM work better than TextCNN on the THUCNews dataset. BiLSTM is better at classification because it uses a bidirectional LSTM, which can capture both above and below semantic features. These two models solve the sequence learning problem, but both models recursively apply the same set of weights to the sequence data to produce a vector representation, which can learn the text dependencies but cannot achieve parallel computation, and the same weight treatment cannot highlight the expressiveness of the key data.