## 1 Introduction  
Target prediction is the prediction of a target associated with an input based on existing data by some method. Target prediction is increasingly important in finance [1, 2], healthcare [3], manufacturing [4], power [5, 6], weather [7], transportation [8], etc. Excellent prediction results can identify potential risks, provide credible prediction data, and improve users' risk management ability; it can develop executable future plans and provide decision-making support for enterprises. Neural networks, decision trees, linear regression, polynomial regression, etc., are methods to achieve target prediction. [PERSON] [9] used multiple target prediction methods, conducted experiments under different numbers of features, feature types, and numbers of samples and concluded that the performance of artificial neural networks with target prediction is better under multi-categorical variable input conditions. Overall, neural networks are the dominant model for solving target prediction problems at this stage. It is important to note that neural network models, such as BP, LSTM, etc., support only numerical data inputs; however, a large portion of the dataset contains non-numerical variables, i.e., categorical variables. For example, the binary categorical variable \"drug reaction\" includes two categories of \"positive\" and \"negative\", and the \"assessment result\" is also a binary categorical variable, including \"pass\" and \"fail\"; another example is the multi-categorical variable \"occupation\", which includes \"teacher, firefighter, construction worker, doctor...\" and other categories. Numerical variables are easier to interpret, but when categorical variables dominate the dataset, it is not easy to view data trends and make predictions [10]. Therefore, a method must be taken to map categorical variables to numerical values before inputting them into the neural network [11]. Categorical variable coding methods include one-hot encoding, label encoding, target encoding, embedding, etc. In this, one-hot encoding is applicable to unordered categorical variables, which generates \(N\)-dimensional binary vectors using 0, 1 for N categories. Label encoding applies to ordered categorical variables, and it maps each category to an integer starting from 0. Target encoding uses the target mean corresponding to the category variables instead of the categorical variables. Embedding, on the other hand, randomly generates weight vectors according to the specified embedding dimensions and maps the text into word vectors according to the indexes of the categorical variables. Unordered multi-categorical variables are categorical variables that contain several (usually more than three) categories with no differences in order or distance between categories. Some existing encoding methods are not entirely suitable for unordered multi-categorical variables; for example, when the number of categories is large, applying one-hot encoding or applying embedding with a large embedding dimension creates the risk of dimensionality explosion, and the data are too sparse after one-hot encoding. Embedding and label encoding also introduce additional order and distance to an originally unordered categorical variable. Target encoding, on the other hand, affects the ability of the neural network model to extract information with the risk of overfitting. In the current research on the target prediction problem, scholars are more concerned about how to design a better model structure to make better prediction results, and when carrying out the input data processing, the data either do not include the categorical variables [12; 13; 14] or use other traditional coding methods. For example, [PERSON] et al. [15] used label encoding for both ordered and unordered categorical variables for data preprocessing when adopting selective integrated learning for ship painting man-hour prediction. [PERSON] et al.