标准化对朴素贝叶斯分类器的影响可以通过以下步骤解决：

1. 导入所需的库：

from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

2. 加载数据集并划分为训练集和测试集：

data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

3. 进行标准化处理：

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

4. 创建朴素贝叶斯分类器并进行训练和预测：

clf = GaussianNB()
clf.fit(X_train_scaled, y_train)
y_pred = clf.predict(X_test_scaled)

5. 输出预测结果：

print("预测结果：", y_pred)

完整的代码示例如下：

from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import GaussianNB
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集并划分为训练集和测试集
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# 进行标准化处理
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 创建朴素贝叶斯分类器并进行训练和预测
clf = GaussianNB()
clf.fit(X_train_scaled, y_train)
y_pred = clf.predict(X_test_scaled)

# 输出预测结果
print("预测结果：", y_pred)

通过标准化处理，可以将特征数据转换为均值为0，方差为1的标准正态分布，这样可以避免特征之间的量纲差异对分类器的影响。