如何高效实现信息分类系统的源代码？

信息分类系统源码是一个用于对大量数据进行有效组织和检索的计算机程序。它通过定义不同的类别和子类别，将相关信息分组存储，便于用户根据特定需求快速查找所需信息。该系统通常包括数据输入、分类规则设定、自动分类处理以及查询输出等功能模块，旨在提高信息管理的效率与准确性。

import os
import re
import jieba
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix
def load_data(data_dir):
    labels = []
    contents = []
    for label in os.listdir(data_dir):
        label_dir = os.path.join(data_dir, label)
        if os.path.isdir(label_dir):
            for file in os.listdir(label_dir):
                file_path = os.path.join(label_dir, file)
                with open(file_path, 'r', encoding='utf8') as f:
                    content = f.read()
                    contents.append(content)
                    labels.append(label)
    return labels, contents
def preprocess_data(contents):
    processed_contents = []
    for content in contents:
        content = re.sub('[^u4e00u9fa5]+', '', content)  # 去除非中文字符
        words = jieba.cut(content)  # 分词
        processed_contents.append(' '.join(words))
    return processed_contents
def train_and_evaluate(X_train, X_test, y_train, y_test):
    vectorizer = CountVectorizer()
    X_train_vec = vectorizer.fit_transform(X_train)
    X_test_vec = vectorizer.transform(X_test)
    clf = MultinomialNB()
    clf.fit(X_train_vec, y_train)
    y_pred = clf.predict(X_test_vec)
    accuracy = accuracy_score(y_test, y_pred)
    cm = confusion_matrix(y_test, y_pred)
    return accuracy, cm
if __name__ == '__main__':
    data_dir = 'path/to/your/data'  # 替换为你的数据目录路径
    labels, contents = load_data(data_dir)
    processed_contents = preprocess_data(contents)
    X_train, X_test, y_train, y_test = train_test_split(processed_contents, labels, test_size=0.2, random_state=42)
    accuracy, cm = train_and_evaluate(X_train, X_test, y_train, y_test)
    print("Accuracy:", accuracy)
    print("Confusion Matrix:")
    print(cm)

这个源码首先加载数据，然后对文本内容进行预处理（去除非中文字符和分词），接着使用朴素贝叶斯分类器进行训练和评估，你需要将data_dir变量替换为你的数据目录路径，该目录下应该包含各个类别的子目录，每个子目录中包含属于该类别的文本文件。